While people have their eyes on the AI race of language models—from OpenAI’s ChatGPT o1 to DeepSeek’s R1, Anthropic’s Claude 3.5, and Google’s Gemini 2.0—some of the most transformative developments in AI are now occurring at the edge, where immediate, on-site processing is redefining business operations. Dubbed “the era of AI inference,” this&nbsp;<a href="https://www.rcrwireless.com/20250210/ai-infrastructure/convergence-of-test-time-inference-scaling-and-edge-ai">next cycle of AI innovation</a> is shifting inference increasingly onto edge devices, thus enhancing accessibility, customizability, and efficiency in AI applications.With 2025 underway, edge AI is rapidly changing how businesses operate by enabling real-time, localized data processing and decision-making. This shift is fueling significant trends across sectors such as autonomous vehicles, IoT, and computer vision. This report examines the evolution of edge AI from a niche technology to a mainstream driver of industry transformation, combining technical analysis with business insights.The first chapter explores the evolving industry trends driving edge AI adoption. It analyzes how sectors like autonomous vehicles, healthcare, manufacturing, and agriculture are increasingly relying on immediate, localized intelligence to improve safety, operational efficiency, and overall performance. The analysis explains the demand for low-latency processing and reduced bandwidth requirements, setting the stage for a shift in data processing and utilization.The second chapter provides a detailed discussion of how edge AI is transforming operational models across industries. By processing data on-site, businesses achieve real-time analytics and decision-making capabilities that traditional centralized systems cannot offer. Specific applications, such as predictive maintenance in manufacturing and real-time patient monitoring in healthcare, illustrate the advantages of deploying AI directly at the source of data generation.In the third chapter, the focus shifts to the technological enablers that support edge AI deployment. Advancements in specialized processors, ultra-low-power devices, and hybrid edge-cloud frameworks, along with software innovations such as edge-native algorithms and hybrid edge-cloud frameworks, are overcoming the challenges of limited processing power and scalability in resource-constrained environments. Moreover, the chapter addresses the critical topic of explainability in edge AI. By integrating lightweight, real-time explainability techniques, developers can ensure that AI decisions are transparent and verifiable, boosting trust in safety-critical applications and regulatory compliance.The fourth chapter examines the collaborative efforts necessary to build a robust edge AI ecosystem. It explains how hardware vendors, software developers, cloud providers, and regulatory bodies are aligning their strategies to create standardized architectures and interoperable platforms. This section emphasizes the importance of partnerships and shared industry frameworks in ensuring that edge AI deployments are secure, scalable, and sustainable.The final chapter presents a forward-looking perspective on the future of edge AI. It explores emerging technologies such as federated learning, quantum neural networks, neuromorphic computing, and the integration of generative AI models. These innovations will drive the development of autonomous systems capable of self-learning and real-time adaptation, reshaping competitive dynamics across industries.This report promises actionable insights and thought leadership that empower decision-makers with a clear roadmap for harnessing edge AI innovation. Through rigorous analysis and industry-focused reporting, readers will gain a deep understanding of the challenges, opportunities, and practical strategies necessary to lead in the era of localized intelligence. Samir JaberReport Editor <h1 dir="ltr">About the Report</h1>This report is the latest installment in the Wevolver Edge AI Technology Reports series. It addresses the pressing need for actionable insights into the exploding field of edge AI and strongly focuses on the industry trends that are reshaping various sectors in 2025. It provides a clear view of the challenges and opportunities facing businesses today.&nbsp;Editor-in-chief Samir Jaber led this initiative, drawing on the rigorous research and insights of co-authors John Soldatos and Deval Shah to form a cohesive narrative on the transformative impact of edge AI.&nbsp;The Wevolver team has been instrumental in orchestrating discussions between contributors, synthesizing expert opinions, and steering the focus towards the most pressing questions. This collaborative effort ensures the report not only delivers actionable insights but also advances Wevolver’s mission to equip engineers, developers, and decision-makers with authoritative analysis that catalyzes industry progress.We extend our gratitude to our sponsors, whose generous support has made this insightful exploration possible. Their commitment to advancing edge AI technology underscores the shared vision of fostering a community poised to lead in the era of localized intelligence. This report stands as a testament to the power of partnership and shared knowledge in navigating the future of technology.Powered by <a href="https://www.froala.com/wysiwyg-editor?pb=1" title="Froala Editor">Froala Editor</a>

Introduction

Industry Trends Driving Edge AI Adoption

The guide to understanding the current state of the art in hardware  software for Edge AI.

2025 Edge AI Technology Report2 !!!!

While people have their eyes on the AI race of language models—from OpenAI’s ChatGPT o1 to DeepSeek’s R1, Anthropic’s Claude 3.5, and Google’s Gemini 2.0—some of the most transformative developments in AI are now occurring at the edge, where immediate, on-site processing is redefining business operations. Dubbed “the era of AI inference,” this&nbsp;<a href="https://www.rcrwireless.com/20250210/ai-infrastructure/convergence-of-test-time-inference-scaling-and-edge-ai">next cycle of AI innovation</a> is shifting inference increasingly onto edge devices, thus enhancing accessibility, customizability, and efficiency in AI applications.With 2025 underway, edge AI is rapidly changing how businesses operate by enabling real-time, localized data processing and decision-making. This shift is fueling significant trends across sectors such as autonomous vehicles, IoT, and computer vision. This report examines the evolution of edge AI from a niche technology to a mainstream driver of industry transformation, combining technical analysis with business insights.The first chapter explores the evolving industry trends driving edge AI adoption. It analyzes how sectors like autonomous vehicles, healthcare, manufacturing, and agriculture are increasingly relying on immediate, localized intelligence to improve safety, operational efficiency, and overall performance. The analysis explains the demand for low-latency processing and reduced bandwidth requirements, setting the stage for a shift in data processing and utilization.The second chapter provides a detailed discussion of how edge AI is transforming operational models across industries. By processing data on-site, businesses achieve real-time analytics and decision-making capabilities that traditional centralized systems cannot offer. Specific applications, such as predictive maintenance in manufacturing and real-time patient monitoring in healthcare, illustrate the advantages of deploying AI directly at the source of data generation.In the third chapter, the focus shifts to the technological enablers that support edge AI deployment. Advancements in specialized processors, ultra-low-power devices, and hybrid edge-cloud frameworks, along with software innovations such as edge-native algorithms and hybrid edge-cloud frameworks, are overcoming the challenges of limited processing power and scalability in resource-constrained environments. Moreover, the chapter addresses the critical topic of explainability in edge AI. By integrating lightweight, real-time explainability techniques, developers can ensure that AI decisions are transparent and verifiable, boosting trust in safety-critical applications and regulatory compliance.The fourth chapter examines the collaborative efforts necessary to build a robust edge AI ecosystem. It explains how hardware vendors, software developers, cloud providers, and regulatory bodies are aligning their strategies to create standardized architectures and interoperable platforms. This section emphasizes the importance of partnerships and shared industry frameworks in ensuring that edge AI deployments are secure, scalable, and sustainable.The final chapter presents a forward-looking perspective on the future of edge AI. It explores emerging technologies such as federated learning, quantum neural networks, neuromorphic computing, and the integration of generative AI models. These innovations will drive the development of autonomous systems capable of self-learning and real-time adaptation, reshaping competitive dynamics across industries.This report promises actionable insights and thought leadership that empower decision-makers with a clear roadmap for harnessing edge AI innovation. Through rigorous analysis and industry-focused reporting, readers will gain a deep understanding of the challenges, opportunities, and practical strategies necessary to lead in the era of localized intelligence.Samir JaberReport Editor<h1 dir="ltr">About the Report</h1>This report is the latest installment in the Wevolver Edge AI Technology Reports series. It addresses the pressing need for actionable insights into the exploding field of edge AI and strongly focuses on the industry trends that are reshaping various sectors in 2025. It provides a clear view of the challenges and opportunities facing businesses today.&nbsp;Editor-in-chief Samir Jaber led this initiative, drawing on the rigorous research and insights of co-authors John Soldatos and Deval Shah to form a cohesive narrative on the transformative impact of edge AI.&nbsp;The Wevolver team has been instrumental in orchestrating discussions between contributors, synthesizing expert opinions, and steering the focus towards the most pressing questions. This collaborative effort ensures the report not only delivers actionable insights but also advances Wevolver’s mission to equip engineers, developers, and decision-makers with authoritative analysis that catalyzes industry progress.We extend our gratitude to our sponsors, whose generous support has made this insightful exploration possible. Their commitment to advancing edge AI technology underscores the shared vision of fostering a community poised to lead in the era of localized intelligence. This report stands as a testament to the power of partnership and shared knowledge in navigating the future of technology.

The Role of Edge AI in Transforming Industry Trends

The guide to understanding the current state of the art in hardware & software for Edge AI.


2025 Edge AI Technology Report1

<h1 dir="ltr" id="isPasted">Introduction</h1>While people have their eyes on the AI race of language models—from OpenAI’s ChatGPT o1 to DeepSeek’s R1, Anthropic’s Claude 3.5, and Google’s Gemini 2.0—some of the most transformative developments in AI are now occurring at the edge, where immediate, on-site processing is redefining business operations. Dubbed “the era of AI inference,” this&nbsp;<a href="https://www.rcrwireless.com/20250210/ai-infrastructure/convergence-of-test-time-inference-scaling-and-edge-ai">next cycle of AI innovation</a> is shifting inference increasingly onto edge devices, thus enhancing accessibility, customizability, and efficiency in AI applications.With 2025 underway, edge AI is rapidly changing how businesses operate by enabling real-time, localized data processing and decision-making. This shift is fueling significant trends across sectors such as autonomous vehicles, IoT, and computer vision. This report examines the evolution of edge AI from a niche technology to a mainstream driver of industry transformation, combining technical analysis with business insights.The first chapter explores the evolving industry trends driving edge AI adoption. It analyzes how sectors like autonomous vehicles, healthcare, manufacturing, and agriculture are increasingly relying on immediate, localized intelligence to improve safety, operational efficiency, and overall performance. The analysis explains the demand for low-latency processing and reduced bandwidth requirements, setting the stage for a shift in data processing and utilization.The second chapter provides a detailed discussion of how edge AI is transforming operational models across industries. By processing data on-site, businesses achieve real-time analytics and decision-making capabilities that traditional centralized systems cannot offer. Specific applications, such as predictive maintenance in manufacturing and real-time patient monitoring in healthcare, illustrate the advantages of deploying AI directly at the source of data generation.In the third chapter, the focus shifts to the technological enablers that support edge AI deployment. Advancements in specialized processors, ultra-low-power devices, and hybrid edge-cloud frameworks, along with software innovations such as edge-native algorithms and hybrid edge-cloud frameworks, are overcoming the challenges of limited processing power and scalability in resource-constrained environments. Moreover, the chapter addresses the critical topic of explainability in edge AI. By integrating lightweight, real-time explainability techniques, developers can ensure that AI decisions are transparent and verifiable, boosting trust in safety-critical applications and regulatory compliance.The fourth chapter examines the collaborative efforts necessary to build a robust edge AI ecosystem. It explains how hardware vendors, software developers, cloud providers, and regulatory bodies are aligning their strategies to create standardized architectures and interoperable platforms. This section emphasizes the importance of partnerships and shared industry frameworks in ensuring that edge AI deployments are secure, scalable, and sustainable.The final chapter presents a forward-looking perspective on the future of edge AI. It explores emerging technologies such as federated learning, quantum neural networks, neuromorphic computing, and the integration of generative AI models. These innovations will drive the development of autonomous systems capable of self-learning and real-time adaptation, reshaping competitive dynamics across industries.This report promises actionable insights and thought leadership that empower decision-makers with a clear roadmap for harnessing edge AI innovation. Through rigorous analysis and industry-focused reporting, readers will gain a deep understanding of the challenges, opportunities, and practical strategies necessary to lead in the era of localized intelligence.Samir JaberReport Editor<h2 dir="ltr">About the Report</h2>This report is the latest installment in the Wevolver Edge AI Technology Reports series. It addresses the pressing need for actionable insights into the exploding field of edge AI and strongly focuses on the industry trends that are reshaping various sectors in 2025. It provides a clear view of the challenges and opportunities facing businesses today.&nbsp;Editor-in-chief Samir Jaber led this initiative, drawing on the rigorous research and insights of co-authors John Soldatos and Deval Shah to form a cohesive narrative on the transformative impact of edge AI.&nbsp;The Wevolver team has been instrumental in orchestrating discussions between contributors, synthesizing expert opinions, and steering the focus towards the most pressing questions. This collaborative effort ensures the report not only delivers actionable insights but also advances Wevolver’s mission to equip engineers, developers, and decision-makers with authoritative analysis that catalyzes industry progress.We extend our gratitude to our sponsors, whose generous support has made this insightful exploration possible. Their commitment to advancing edge AI technology underscores the shared vision of fostering a community poised to lead in the era of localized intelligence. This report stands as a testament to the power of partnership and shared knowledge in navigating the future of technology.Powered by <a href="https://www.froala.com/wysiwyg-editor?pb=1" title="Froala Editor">Froala Editor</a>

2025 Edge AI Technology Report

A GPU microchip for accelerated AI processing

Unleashing the Power of GPU, understanding the essential strategies for efficient Deep Learning Model Training without compromising on memory and hardware resources in Graphical Processing Units

Maximize GPU Utilization for Model Training: Unlocking Peak Performance

Continual advancements in space technology are reshaping our capacity to manage Earth's affairs with precision and efficiency. Amidst the complexities of urbanization, climate change, and global connectivity, the need for sophisticated infrastructure has become increasingly apparent.&nbsp;Satellites, with advanced functionalities, have emerged as indispensable tools for data acquisition, analysis, and communication, which are crucial for informed decision-making on Earth.With their high-resolution cameras, multispectral sensors, and robust communication capabilities, we can capture and transmit data from a wide range of Earth-related activities, from urban planning and disaster management to agricultural monitoring and environmental protection.By continuously orbiting the Earth, satellites capture detailed images and other sensor data covering the whole planet’s surface. We can use this data to track changes in landscapes, monitor weather patterns, and observe natural disasters like forest fires, floods, and hurricanes in real time.This data is collected and transmitted back to Earth, where it can undergo rigorous analysis. Machine learning algorithms, data analytics, and other advanced computing techniques allow us to interpret the data with ease. For example, predictive modeling is utilized to forecast weather changes or track the migration patterns of endangered species. The ability to process real-time data enables quick decision-making, which is especially crucial during responses to natural disasters and emergencies.&nbsp;Communication is another critical functionality of satellites. They ensure communication lines are able to remain open, even in the most remote parts of the world. This facilitates global connectivity, allowing data, voice, and video communication to be transmitted across diverse geographical landscapes.<h1 dir="ltr">Maintenance in space</h1>On Earth, if something breaks down in a factory or a power plant, we don’t build a replacement. Instead, we send a repair team with spare parts, and even better yet, we monitor the components' wear and replace them before they break down. This preventive maintenance approach minimizes downtime and reduces the costs associated with sudden breakdowns.This methodology is critical in industries where reliability and uptime are essential. By continuously monitoring equipment health through sensors and predictive analytics, we can identify potential problems before they lead to operational failures. However, this approach faces unique challenges in space, where sending a repair team is not feasible due to the vast distances and extreme conditions.The transition from Earth-based maintenance to space requires innovative solutions to overcome these challenges. The harsh space environment, including vacuum conditions, microgravity, and cosmic radiation, makes traditional maintenance methods impractical. Additionally, the delay in communication between Earth and spacecraft further complicates real-time interventions, necessitating a more autonomous approach to maintenance and repair operations in space.<a href="https://www.ant61.com/" target="_blank" rel="noopener noreferrer">ANT61&nbsp;</a>develops autonomous robots that enable the maintenance of satellites without the need to put human lives at risk. ANT61 is providing solutions for both: their Beacon product allows satellite operators to understand what went wrong with their satellites and restore them back to operation.For larger and more expensive satellites, they are building robots that will dock, refuel, and, in the future, refurbish satellites, prolonging their useful life. At the core of these robots lies ANT61 BrainTM, the innovative devices that combine machine vision and decision-making technology, enabling the autonomy of these maintenance robots. Autonomy is very important as, due to the speed of light limitations, it won’t be possible to control every movement of these robots remotely from Earth.<h3 dir="ltr">BrainChip AkidaTM&nbsp;: A revolutionary neuromorphic processor for edge AI applications</h3>The first generation of the ANT61 Brain uses the <a href="https://brainchip.com/akida-neural-processor-soc/" target="_blank" rel="noopener noreferrer">BrainChip AkidaTM</a> chip for power-efficient AI and is currently on board the Optimus-1 satellite, which was deployed recently by the SpaceX Transporter-10 mission. ANT61 will test the ANT61 Brain later this year and perform training and evaluation of various neural networks that we will use for future in-orbit servicing technology demonstrations.The BrainChip Akida™ chip is a groundbreaking advancement in neuromorphic computing, designed to mimic the neural architecture of the human brain. This technology stands out for its ability to process data directly at the point of acquisition, greatly enhancing efficiency by reducing the reliance on distant cloud-based systems for data processing.Akida's unique capability lies in its event-based neural processor, which processes data only when needed, using data sparsity to significantly reduce the number of operations required. This leads to lower power consumption and heat dissipation, making it highly suitable for the demanding conditions of space. Each Akida node consists of four Neural Network Layer Engines (NPEs), which can be configured for various functions, such as convolutional layers or fully connected layers, and support incremental learning and high-speed inference. This configuration flexibility allows the chip to efficiently handle a wide range of AI tasks without needing constant connectivity or high power draw.Akida's scalable architecture enables the chip to support up to 256 nodes in a mesh network, boosting computational power while maintaining a small footprint. This scalability is crucial for space applications where efficiency and performance are vital. The entire neural network can be embedded directly within the Akida fabric, eliminating the need for frequent data transfers that consume significant power and reduce system responsiveness.Another key feature of the Akida chip is its on-chip learning capability, which allows it to adapt and improve its performance over time without external intervention. This feature is particularly beneficial in space, where conditions can change rapidly and autonomy is crucial. By processing and learning from data locally, Akida enhances the autonomy of satellite maintenance robots like those developed by ANT61, enabling them to perform complex tasks with minimal latency and enhanced reliability.The ANT61 team chose to partner with BrainChip because they believe that neuromorphic technology will bring the same exponential improvement in AI as 20 years ago. The shift from CPU to GPU opened doors to deep neural network applications, which are at the core of all AI technologies today. Neuromorphic technology is also perfect for space: the lower power consumption means less heat dissipation, and we can get up to five times more computational power for the same electrical power budget.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1715339791069-Akida_1000_chip-1.jpg" style="width: 100%px;" class="fr-fic fr-dib">Akida AKD1000 is a reference chip implemented with TSMC at 28nm<h1 dir="ltr">The ANT61 vision for the future</h1>Humanity’s expansion to the cosmos requires infrastructure that can only be built by robots. With its in-orbit servicing experience, ANT61 aims to become the main supplier of the robotic workforce for Moon and Mars installations. This will enable companies from Earth to project their ambition to space, providing valuable services and resources for future off-world factories and cities.Neuromorphic computing, exemplified by technologies like the BrainChip Akida™ chip, is set to play a pivotal role in this spacefaring era. This form of computing brings several advantages crucial for space applications, such as low power consumption, high efficiency, and the ability to perform complex computations needed for autonomy. By mimicking the human brain, neuromorphic processors can process large volumes of sensor data efficiently, making real-time decisions without the need to send data back to Earth for processing.The integration of neuromorphic computing into space robotics promises to significantly improve these machines' capabilities. Robots equipped with advanced AI processors can better handle the unpredictable nature of space environments, adapting to new challenges autonomously and performing tasks ranging from construction to maintenance and repair. This capability ensures that space habitats are built efficiently and can be sustained with minimal human oversight.ANT61's vision of utilizing these technologies to support human expansion into space reflects a forward-thinking approach that will not only facilitate the construction of off-world establishments but also ensure their long-term sustainability and success.&nbsp;

Remote satellite maintenance is crucial to critical data acquisition and global communication


Advancing Earth Management Through Neuromorphic Computing Powered Satellites

<h2 dir="ltr" id="isPasted">Join Us for Our Upcoming Webinar: Powering Edge and Endpoint Applications: A deep dive into Reality AI Tools® from Renesas</h2>Mohammed Dogar, Vice President, Head of Global Business Development &amp; Ecosystem, and Jeff Sieracki, Director of AI COE, will sit with tech writer Samir Jaber to unpack the <a href="https://www.renesas.com/us/en/products/microcontrollers-microprocessors/reality-ai#featured_products" target="_blank" rel="noopener noreferrer">Reality AI Tools</a> suite and discuss their impact on Edge AI applications.Why Attend?<ul><li dir="ltr">Learn:&nbsp;Understand the primary functions and benefits of using Reality AI Tools®</li><li dir="ltr">Connect: Hear from Mohammed Dogar, Vice President, Head of Global Business Development &amp; Ecosystem, and Jeff Sieracki, Director of AI COE</li><li dir="ltr">Engage: Opportunities to ask questions and interact with our experts</li></ul>Event Details:<ul><li dir="ltr">Topic: Powering Edge and Endpoint Applications: A deep dive into Reality AI Tools® from Renesas</li><li dir="ltr">Date:&nbsp;May 28th 2024</li><li dir="ltr">Time:&nbsp;5pm CET (add others)</li><li dir="ltr">Location:&nbsp;Online (via Zoom). Registrants will be sent the webinar video after the event.&nbsp;</li></ul><a href="https://us02web.zoom.us/webinar/register/4017151721849/WN_ahhKxtJ0TuWs-Klj5BfGgg" target="_blank" rel="noopener noreferrer" class="button-main-action">Register for free here</a><h2>Webinar speakers</h2><h3>Mo Dogar</h3>Mo Dogar is head of Global Business Development and Technology Ecosystem at Renesas Electronics, responsible for promotion and business expansion of the complete Microcontroller portfolio and other key products. He is instrumental in driving global business growth and alignment of marketing, sales and development strategies particularly in the areas of IoT, e-AI, Security, smart embedded electronics and communications.&nbsp;In addition, Mr. Dogar helps provide the vision and thought leadership behind product and solution development smart society and the evolving IoT economy. With extensive experience as a keynote speaker and thought leader in the electronics industry, recent speaking engagements include keynote addresses at CES, Arm Techcon, Embedded World, Industrial Open Day and a number of other AIoT and electronics events globally. He studied Electronics &amp; Information Engineering at the University of Huddersfield, UK and received Project Management qualifications with an MBA from the Open University in the UK and has worked for a number of industrial and technology companies including Rockwell Automation. In 2001 he joined NEC and subsequently Renesas and has worked in various roles including Engineering, Applications, Marketing and Business Development. &nbsp;<h2>Dr. Jeff Sieracki</h2>Dr. Jeff Sieracki is an extremely applied mathematician who has focused his career on the intersection of modern signal processing and machine learning. Former co-founder and CTO of Reality AI, he joined Renesas as part of an acquisition in 2022 and is now head of AI Engineering at the Renesas AIoT Center of Excellence. Jeff’s work spans signal and sensing problems in commercial industry, medical devices, and DoD applications. He has started, built, and exited three companies, one in each of those application areas. He has been awarded more than 50 technology patents. Jeff holds a PhD in Applied Mathematics and Scientific Computing and was a founding member of the Norbert Weiner Center for Harmonic Analysis and Applications at the University of Maryland, College Park. He holds a Masters in Applied Mathematics and a BS in Physics from the John Hopkins University.<h2>Samir Jaber</h2>Samir Jaber is an editor, writer, and industry expert on topics of technology, science, and engineering. He is the editor-in-chief of the 2024 State of Edge AI and 2023 Edge AI Technology reports with Wevolver. Samir is the Chief Editor and Founder of Wryters, a content marketing and consulting agency. He has comprehensive experience working with Fortune 500 companies and industry leaders as a writer, editor, content manager, and consultant. He is an online content specialist with an academic background in mechanical engineering, nanotechnology, and scientific research. Samir is also a featured author in 30+ industrial magazines with a focus on Artificial Intelligence (AI), the Internet of Things (IoT), 3D printing, Autonomous Vehicles (AV), nanotechnology, materials science, and sustainability. His experience includes award-winning engineering research and patented engineering design in the fields of nanofabrication and microfluidics.This event is a must-attend for all engineers interested in the tools enabling AI at the edge. If you can't make it on the day, all registrants will be sent the webinar recording.&nbsp;&nbsp;<a href="http://Samir Jaber is an editor, writer, and industry expert on topics of technology, science, and engineering. He is the editor-in-chief of the 2024 State of Edge AI and 2023 Edge AI Technology reports with Wevolver. Samir is the Chief Editor and Founder of Wryters, a content marketing and consulting agency. He has comprehensive experience working with Fortune 500 companies and industry leaders as a writer, editor, content manager, and consultant. He is an online content specialist with an academic background in mechanical engineering, nanotechnology, and scientific research. Samir is also a featured author in 30+ industrial magazines with a focus on Artificial Intelligence (AI), the Internet of Things (IoT), 3D printing, Autonomous Vehicles (AV), nanotechnology, materials science, and sustainability. His experience includes award-winning engineering research and patented engineering design in the fields of nanofabrication and microfluidics." target="_blank" rel="noopener noreferrer" class="button-main-action">Register for free now</a>

Credit: Evan Stoddard, Advanced Applications Group Embedded Software Engineer at Avnet

Mohammed Dogar, Vice President, Head of Global Business Development & Ecosystem, and Jeff Sieracki, Director of AI COE, will sit with tech writer Samir Jaber to unpack the Reality AI Tools suite and discuss their impact on Edge AI applications.

Webinar: Powering Edge and Endpoint Applications - A deep dive into Reality AI Tools® from Renesas

Space exploration has captivated humanity for decades with its promise of adventure and scientific discovery. But venturing beyond our atmosphere comes with immense challenges. This is where a new wave of technology is poised to revolutionize the space industry: artificial intelligence (AI) and robotics.&nbsp;This episode of the BrainChip podcast is a conversation between BrainChip CTO Dr Tony Lewis and Mikhail Asavkin, founder of Australian space tech company ANT61. They talk about how AI and intelligent robots are no longer science fiction but the key to unlocking the future of space exploration.&nbsp;<h2 dir="ltr">Reusable Rockets and the Democratization of Space</h2>Over the past decade, we have witnessed a remarkable transformation propelled by innovation and the rise of private industry in the space sector. The ability to recover and relaunch rockets has dramatically reduced the cost of space access, opening the door for a new era of private space exploration companies.&nbsp;This democratization of space has spurred innovation and ignited a global race to push the boundaries of what's possible. Mikhail Asavkin, drawing from his extensive experience in space engineering, sheds light on this evolutionary journey and explains how companies like ANT61 are now able to focus their resources on cutting-edge technologies like AI and robotics, thanks to the more affordable launch options provided by reusable rockets.&nbsp;This shift has fundamentally changed the landscape of space exploration, making it a more accessible and dynamic field.<h2 dir="ltr">The Impact of AI on Space Exploration</h2>Mikhail Asavkin and Dr Tony Lewis also discuss the transformative role of AI in space exploration. Here's why AI is the silent hero propelling us further into the cosmos:<ul><li dir="ltr">Due to the speed of light, communicating with robots on the Moon or Mars in real time is impossible. This is where AI steps in. By enabling robots to make autonomous decisions based on sensor data and their surroundings, AI eliminates the need for constant human intervention from Earth.</li><li dir="ltr">Space exploration demands more than just following pre-programmed instructions. Robots must be able to perceive their environment, identify objects, and navigate complex situations. Advancements in AI, particularly image recognition and deep learning, allow robots to "see" and understand their surroundings, enabling them to perform intricate tasks and make critical decisions independently.</li></ul><h2 dir="ltr">Neuromorphic Computing in Space</h2>The limitations of traditional AI systems, particularly their high power consumption, pose a significant challenge for space exploration. Here's where neuromorphic computing enables new ways of exploring space.&nbsp;Inspired by the structure and function of the human brain, neuromorphic chips are designed to process information similarly. This translates to a dramatic reduction in power consumption compared to traditional AI hardware like GPUs (Graphics Processing Units). For spacefaring robots, this is a game-changer.Neuromorphic technology offers a multitude of benefits for space AI:<ul><li dir="ltr">Neuromorphic chips enable this dream by requiring significantly less power to perform complex tasks like object detection and real-time decision-making.</li><li dir="ltr">Neuromorphic chips excel at real-time processing, allowing robots to make critical decisions without delay. This is essential for tasks like navigating hazardous terrain or performing delicate scientific operations.</li></ul><h2 dir="ltr">Training AI on the Go</h2>Traditionally, training AI models involves massive datasets downloaded from Earth and meticulously labeled by humans – a time-consuming and impractical process in the vast expanse of space.Neuromorphic computers, with their ability to learn and adapt in real time, make this possible. The AI model can continuously improve its capabilities and decision-making abilities by processing sensor data and surroundings. By training on-site, the AI model can adapt to specific situations and environments, making its tasks more robust and efficient.However, the podcast also discusses a significant challenge that remains: ensuring the safety and well-being of human explorers venturing into the unknown. This necessitates further advancements in AI, particularly in areas like risk assessment, anomaly detection, and real-time decision-making.<h2 dir="ltr">Conclusion: New Era in Space Exploration</h2>From enabling autonomous robots to construct lunar habitats to empowering AI models to learn and adapt on the fly, these technologies are revolutionizing how we venture beyond our planet. As we look towards a future where humanity establishes a presence on other celestial bodies, AI and robotics will be our indispensable companions.Listen to the full podcast <a href="https://www.youtube.com/watch?v=yyVWVjYM-nQ&t=3s" target="_blank" rel="noopener noreferrer">here.</a>

In this episode, ANT61 Founder and CEO Mikhail Asavkin joins BrainChip CTO Dr. Tony Lewis to discuss the evolution of Space Technology and its impact on daily life. 

BrainChip Podcast: Robotics in Space discussion with ANT61 Founder Mikhail Asavkin

Have you ever wondered how the next generation of space exploration would be powered? The answer might lie in artificial intelligence (AI), and a new frontier is emerging: AI in space.&nbsp;This episode of the Brain Chip podcast explores the vital role of BrainChip in pushing AI to new frontiers through Akida, which was recently launched into space aboard the Transporter 10 spacecraft, alongside Dr. Tony Lewis, BrainChip’s CTO, and experts from the European Space Agency (ESA) Laurent Hili and Luis Mansilla.<h2 dir="ltr">The Rise of New Space&nbsp;</h2>The podcast begins by highlighting the dramatic shift in space exploration. Gone are the days of solely government-driven endeavors. Private companies like SpaceX are making space more accessible, leading to a new era of "New Space." This increased activity necessitates advancements in spacecraft autonomy and mission efficiency.One solution gaining traction is AI. Implementing AI systems directly on spacecraft can automate complex tasks, freeing human astronauts for critical decision-making and exploration activities. Additionally, onboard AI can analyze data in real time, enabling faster responses to unforeseen situations and optimizing mission parameters. However, the podcast emphasizes the challenges of using traditional AI hardware in space.<h2 dir="ltr">Neuromorphic Computing: A Promising Path Forward</h2>The podcast also explores the complexities of implementing AI in space. One of the most significant hurdles is power consumption. Traditional AI hardware can be power-hungry, a major constraint for spacecraft with limited energy resources. As discussed, traditional computing struggles with the power constraints of space.&nbsp;This is where neuromorphic computing emerges as a game-changer. Unlike conventional processors that rely on the von Neumann architecture, neuromorphic computing takes inspiration from the human brain.Here's how neuromorphic computing breaks the mold:<ul><li dir="ltr">Mimic the brain's structure for parallel processing and potential efficiency gains.</li><li dir="ltr">Consume significantly less power, which is crucial for space where every watt counts.</li><li dir="ltr">Can be tailored for specific AI tasks, further boosting efficiency.</li></ul>While still young, neuromorphic computing holds immense promise for space AI.This is where AI comes in. Artificial intelligence has the potential to revolutionize space exploration by enabling:<ul><li dir="ltr">Onboard decision-making: AI algorithms could analyze sensor data and make real-time decisions without relying on constant communication with Earth, which vast distances and time delays can hinder.</li><li dir="ltr">Autonomous operations: Spacecraft equipped with AI could perform tasks like navigation, anomaly detection, and even scientific data analysis with minimal human intervention.</li><li dir="ltr">Data reduction and filtering: AI can sift through vast amounts of data collected by space instruments, identifying valuable information and reducing the amount that needs to be transmitted back to Earth, saving precious bandwidth.</li></ul><h2 dir="ltr">ESA's Multifaceted Approach to AI in Space</h2>The podcast goes beyond just neuromorphic computing, showcasing the multifaceted approach of the European Space Agency (ESA) to integrating AI into various space missions. As highlighted by ESA, they are exploring AI applications across different departments, including:<ul><li dir="ltr">Beamforming and anti-jamming technologies for improved communication and signal strength.</li><li dir="ltr">Maritime applications such as ice bear detection for environmental monitoring.</li><li dir="ltr">Earth observation tasks like analyzing data from the new SC of the Cloud.</li></ul>They also discussed ESA's adoption of the NASA Technology Readiness Level (TRL) system to assess the maturity of AI technologies for space use. This standardized approach helps prioritize development efforts and ensures that technologies are adequately tested before flight.<h2 dir="ltr">BrainChip's AK100</h2>The podcast discusses BrainChip's AK100 chip and its potential role in ESA's space missions. BrainChip reveals that the AK100 chip is currently at TRL 3, indicating it's in the early stages of development. Further work is required to harden the technology for space environments, including rigorous testing for radiation resistance and mitigation strategies.<h2 dir="ltr">Conclusion: A Collaborative Future for AI in Space</h2>The BrainChip podcast episode offered a glimpse into the exciting and complex world of AI in space. While challenges remain, particularly regarding power consumption and technology validation, the potential benefits of AI for space exploration are undeniable. The collaboration between ESA and BrainChip exemplifies the innovative spirit driving this new frontier.

Listen in as Dr. Tony Lewis, BrainChip's CTO, Laurent Hili (Microelectronics Engineer), and Luis Mansilla (AI Expert) discuss the growing role of AI in space exploration, ESA's investments in this technology, and how BrainChip's Akida chip contributes to this exciting frontier

BrainChip Podcast: Insights on SpaceTech With European Space Agency ESA.

Embedded World 2024, held at Nuremberg Messe from April 8 to 11, was a brilliant event for all engineers, designers, and researchers involved in embedded systems.&nbsp;With almost a thousand exhibitors and a huge schedule of related events, Embedded World has really lived up to its reputation of showcasing the industry's cutting-edge.&nbsp;Wevolver team members took the chance to meet engineers from all over the world. Here are some of the highlights.&nbsp;<h2>OKdo: LLMs at the edge.&nbsp;</h2>At Embedded World 2024, <a href="https://www.wevolver.com/profile/okdo" target="_blank" rel="noopener noreferrer">OKdo</a> - a key player in single-board computing and IoT solutions - &nbsp;showcased its significant advancements in industrial computing technologies. One of the highlights was the display from <a href="https://www.liquid.ai/" target="_blank" rel="noopener noreferrer">Liquid AI</a>. Liquid AI is developing a new generation of AI foundation models built from first principles - going beyond generative pre-trained Transformers (GPTs). Liquid neural net technologies unlock efficiency, robustness, explainability, and state-of-the-art performance. Generative AI on the edge (for exampl,e on a Raspberry Pi 5 or Rock 5a) becomes possible.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzOTUxMTA5ODUwLU9LIHN0YW5kLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">The OKdo Embedded World stand. Credit: OKdo.<h2>Enabling cutting-edge PCB design</h2>Altium, a PCB design software company, &nbsp;introduced its new enterprise-grade system, Electronics Lifecycle Management (ELM), a system for enterprise-level hardware design and development, which was a featured presentation in the Exhibit Hall. The booth experience also featured live demos of solutions, including Altium Designer, Altium 365, Nexar API, and Octopart. Altium is transforming electronic development by allowing anyone in the electronics industry to connect with team members and partners and with solutions and data to work more productively.&nbsp;“Altium is uniquely positioned to reshape the future of electronics development and production. We were extremely excited to share our vision of a transformed industry ― which is fast becoming a reality ― with the world’s leaders in embedded technology at Embedded World 2024.” – VP Enterprise Marketing, Altium, Naomi Hamels&nbsp;See Altium in action below:&nbsp;<iframe width="640" height="360" src="https://www.youtube.com/embed/aZeWMmDGFKg?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="253684619" title="Altium at Embedded World 2024" data-gtm-yt-inspected-23="true">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</iframe><h2>Exploring NXP's advancements in motor control and TSN communication&nbsp;</h2>At Embedded World 2024, NXP showcased advancements in smart edge technologies aimed at improving device automation and connectivity. They introduced new technologies and products for various markets, including automotive, industrial, and smart home sectors. Demonstrations highlighted NXP's solutions for software-defined vehicles, smart home interoperability, and industrial automation. The event also featured expert-led tours and demonstrations exploring AI, edge computing, and more.<iframe width="640" height="360" src="https://www.youtube.com/embed/fFeo0q6AdWA?&amp;feature=youtu.be&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="597216751" title="Wevolver at Embedded World: NXP Semiconductors" data-gtm-yt-inspected-23="true">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</iframe><h2>The latest in automotive local dimming control with Synaptics</h2>AT EW 2024, Synaptics launched its <a href="https://www.synaptics.com/products/embedded-processors" target="_blank" rel="noopener noreferrer">Astra platform</a> of AI-native embedded processors designed to enhance the capabilities of Internet of Things (IoT) devices. This platform focuses on providing multi-modal, AI-enabled microprocessors (MPUs) and microcontrollers (MCUs) that are optimized for power efficiency and high performance. Astra supports a unified software experience across various operating systems and includes an adaptive AI framework, which facilitates the integration of AI into devices. It also offers robust connectivity solutions, such as Wi-Fi and Bluetooth, and is designed to help developers easily incorporate AI features into their products.During our visit, we also got to see their SmartBridge technology live. This advanced automotive display processor is a local dimming IC that supports large, high-contrast, high-resolution automotive LCDs while consuming lower overall power and cost. SmartBridge can handle up to 30-inch displays and 6k resolutions. It leverages a proprietary algorithm to analyze incoming images and control the brightness of each LED in the array.&nbsp;<iframe width="640" height="360" src="https://www.youtube.com/embed/xGelGpfUJFw?&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="506257900" title="Wevolver at Embedded World: Synaptics Incorporated" data-gtm-yt-inspected-23="true">&nbsp;&nbsp;&nbsp;&nbsp;</iframe><h2>Code reading with MVTec Software</h2>At the Embedded World 2024, <a href="http://www.mvtec.com" target="_blank" rel="noopener noreferrer">MVTec Software</a> showcased its extensive portfolio of machine vision software, prominently featuring their products HALCON and MERLIC. These products are designed to integrate seamlessly with various hardware configurations, supporting a broad spectrum of operating systems, AI accelerators, CPUs, and embedded boards. The company emphasized the adaptability of their software to different industrial needs, making it suitable for smart cameras, sensors, and other embedded vision applications.MVTec's presentation revolved around the theme "Embedded Vision = YOUR Hardware + OUR Software," underscoring the customizable nature of their solutions to enhance hardware capabilities with sophisticated vision software. This approach not only facilitates enhanced performance and compatibility across different machine vision standards like GigE Vision, GenICam, and USB3 Vision but also promotes quicker time-to-market for products developed using their systems. The technologies highlighted include advanced features for code reading, optical character recognition (Deep OCR), and deep learning applications such as anomaly detection.<iframe width="640" height="360" src="https://www.youtube.com/embed/CHitadg5uKg?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="37302711" title="Wevolver at Embedded World: MVTec Software Demo" data-gtm-yt-inspected-23="true">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</iframe><h2>The 2024 State of Edge AI Report</h2>At Embedded World we were thrilled to share the 2024 State of Edge AI Report with you. &nbsp;This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.<h3>Report Highlights:</h3>#1: Industry-specific insights– Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.We hope you download the report below!&nbsp;<iframe width="540" height="665" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" data-gtm-yt-inspected-23="true">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</iframe>

Wevolver was present at the recent Embedded World event in Nuremberg to launch the 2024 Edge AI report. 


Embedded World 2024: Trends, Tech, and Trailblazers

Our new report, AI at the Edge: Technology Accelerated by Wi-Fi Services, dives deeply into the convergence of Wi-Fi services and Edge AI and explores how a technology model like WaaS can accelerate the adoption of AI at the edge.&nbsp;Below is an excerpt from the final chapter of the report. To read the full report, including in-depth case studies, download it now.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzExMTI0MjE4NTYyLVJlbGF5IDIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;</iframe>The next years of this decade are going to be extremely exciting for AI, particularly Edge AI. And with Wi-Fi Services like Relay2 revolutionizing the access point market, the future is going to be very promising indeed. The surging demand for AI applications that offer real-time, low-latency, and privacy-conscious capabilities will usher in a wave of Edge AI implementations. These deployments are poised to become increasingly precise and efficient, capitalizing on advancements in communication networks, such as Wi-Fi 6, coupled with innovations in artificial intelligence (such as data-efficient AI) and the Internet of Things (IoT).&nbsp;This transformative landscape underscores the pivotal role played by researchers, industry leaders, and open-source communities in steering the trajectory of Edge AI development. The evolution of efficient Edge AI applications relies on the progress of various technologies designed to support edge devices and enhance the computational efficiency of AI functions, including Wi-Fi services.<h3 dir="ltr">Increased Adoption &amp; Integration with Edge AI</h3>As the demand for real-time processing and low-latency applications continues to surge, the integration of Edge AI with Wi-Fi services will follow suit. The agility and scalability of Wi-Fi seamlessly complement the requirements of edge computing and AI. For instance, the latest generation of Wi-Fi, Wi-Fi 6, promises much faster data transfer speeds that can reach up to five times the speeds of Wi-Fi 5. This is complemented by reduced power usage, increased capacity, and enhanced performance, providing higher connection reliability in environments with multiple connected devices. This is where Relay2 sets a standard with its Wi-Fi services, pushing for innovation that leverages advancements like Wi-Fi 6. With a dynamic system that ensures flexibility and security, Relay2 offers virtual access control and simultaneous HD video sessions that can support up to 128 sessions. This is further supported by high performance and management of heavy traffic, capitalizing on an average throughput DL that exceeds 2 Gbps and handles 1,024 clients concurrently.This is bound to grow and enhance the adoption and integration with Edge AI in order to enrich the user experience. By implementing AI at the edge for efficient data processing and leveraging Relay2’s proactive real-time analysis and natural language adaptability, businesses and organizations can elevate their user experience beyond merely improving connection reliability. This synergy allows for efficient processing closer to data sources, reducing latency and enhancing overall system responsiveness.With Wi-Fi 7 being the next generation of Wi-Fi, we could see an even more seamless and immersive user experience and an advanced Edge AI performance, building on its new features such as multi-link operation (MLO), 320 MHz superwide channels, and a 4k quadrature amplitude modulation (QAM). This convergence of state-of-the-art communications and information technologies, such as cloud-native Wi-Fi services, edge computing, IoT, and AI, is driving a massive leap in management efficiency and social productivity. The hybrid edge-cloud architectures and applications are likely to surge in the very near future, ensuring not only high speed and efficiency but also the ability to run multi-modal generative AI and language models at the edge . In the second half of this decade, we will arguably witness an unprecedented increase in the adoption of Edge AI across numerous applications, rendering the technology mainstream.<h3 dir="ltr">Computer Vision &amp; the Impact of Edge AI and Wi-Fi</h3>One area where the convergence of Edge AI and Wi-Fi services is poised to have a high impact is computer vision applications. With the capability to process data locally, Wi-Fi-empowered edge devices can execute intricate computer vision tasks with unparalleled speed and efficiency. This transformative shift not only minimizes reliance on centralized cloud servers but also allows positioning systems to optimize energy and costs, enabling applications such as facial recognition, object detection, and automated surveillance to operate seamlessly in real time.With localization techniques such as Wi-Fi-based positioning systems (WPS) and the advent of convolutional neural networks (CNNs), we can detect and extract spatial patterns from visual data in a remarkable manner. This has the potential to transform industries, including healthcare, retail, automotive, and traffic management. Combining the high-processing capabilities of cloud computing with the real-time features of edge computing and the connectivity enhancements brought about by Wi-Fi technologies can enable AI to lift computer vision to a different level. Consider industrial automation in large factories and warehouses, smart cities with optimized traffic control, retail centers and shopping complexes with enhanced services and store layouts based on monitored customer behavior, and even remote medical services and patient monitoring that enable accurate diagnosis in real time. All these developments can take place by leveraging computer vision that is powered by AI at the edge.While there are still challenges standing before computer vision today, such as high processing power and high-quality data, the potential that Edge AI brings, supported by Wi-Fi capabilities that ensure fast and seamless data transfer - even in remote areas where accessibility would otherwise be difficult - will propel computer vision and enable it to be a much more potent and ubiquitous technology.&nbsp;With on-device inference capabilities and functionalities that enable computer vision within edge and mobile devices, image and pattern recognition can improve significantly, taking advantage of edge computing’s reduced latency, enhanced security, and minimized costs. Integrating AI into the mix enables even low-cost cameras to act as smart cameras, with deep learning methods enabling better identification and interpretation of data patterns. This is further enhanced by leveraging data from multiple devices connected via Wi-Fi, which provides AI with more comprehensive data, reinforcing its precision and understanding.<h2 dir="ltr">Conclusion: Wi-Fi-Powered Edge AI - A Technology for the Future</h2>Wi-Fi and Edge AI represent a technological paradigm shift with far-reaching implications. The convergence of Wi-Fi with Edge AI is not just a technological evolution but a fundamental reshaping of the digital landscape. As this symbiotic relationship matures, it propels us towards a future where Edge AI is not just a technology but an integral and indispensable part of our interconnected world. The potential for transformative applications is immense, urging both developers and sellers to explore the uncharted territories of innovation and contribute to the unfolding narrative of Edge AI's promising future.This not only addresses the computational needs of Edge AI but also aligns with the growing trend of connected devices in smart buildings, industrial settings, healthcare, retail, and smart cities. The integration of Edge AI with Wi-Fi services brings forth a boost in performance, user experience, and security measures, ensuring that sensitive data is processed locally. This transformation leads to the development of robust, always-on systems capable of meeting the stringent privacy requirements of contemporary applications and the ever-growing need for better performance, flexibility, scalability, and reliability.Throughout this report, we explored the capabilities and impact of Edge AI, especially when coupled with Wi-Fi services. We highlighted the valuable insights provided by Relay2, the leading Wi-Fi technology provider for the Edge AI market. Relay2's Service Points technology is designed to deliver exceptional application performance while enhancing security, privacy, and scalability at the edge.&nbsp;The Relay2 ServiceEdge Platform harnesses the power of Wi-Fi Services with Edge Computing to create a digital world where application performance and user experience not only remain uncompromised but are also enhanced. We also dove into particular applications and reviewed some use cases of Wi-Fi services edge computing. And finally, we took a look at the future of these technologies and how they will impact our businesses and way of living in the coming years. All this could not have been done without the support of Relay2 in developing this report.&nbsp;Relay2 is committed to empowering its partners by providing comprehensive, innovative solutions to ensure mutual success leveraging Wi-Fi Services with Edge Computing and the Relay2 ServiceEdge Platform. Therefore, developers and sellers are encouraged to partner with Relay2 to unlock the full potential of technology and shape a seamless digital future. The convergence of Wi-Fi with Edge computing can reshape the digital technology and business landscape, and Relay2 is at the forefront of this transformation.<h2 dir="ltr">Acknowledgments</h2>This report is the result of collaborative efforts between Wevolver and Relay2. Initial input was graciously provided by Relay2’s Eric Chen and Daniel Kwong, sharing their insights, expertise, and case studies, creating a robust technical substructure for the report. Then, the author further expanded upon this information to develop a comprehensive and insightful text, supported by the content and design team at Wevolver. Thank you to all contributors and supporters, and special thanks to Relay2.Download the full report with custom images, case studies and more, below.&nbsp;<iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;</iframe><h3 dir="ltr" id="isPasted">Read excerpts of the report chapters:</h3>Chapter 1: <a href="https://www.wevolver.com/article/ai-at-the-edge-report-chapter-1-wi-fi-analytics-services-enabling-edge-ai" rel="noopener noreferrer" target="_blank">Wi-Fi Services Enabling Edge AI</a>Chapter 2: <a href="https://www.wevolver.com/article/ai-at-the-edge-report-chapter-2-relay2-and-edge-ai" rel="noopener noreferrer" target="_blank">Relay2 and Edge AI</a>&nbsp;Chapter 3: <a href="https://www.wevolver.com/article/ai-at-the-edge-report-chapter-3-applications-case-studies" rel="noopener noreferrer" target="_blank">Applications &amp; Case Studies</a>

The coming years will see a surge in Edge AI advancements, driven by real-time, efficient, and privacy-focused AI applications, alongside innovations in Wi-Fi and IoT, marking a promising future shaped by researchers, industry leaders, and open-source contributions.

AI at the Edge Report. Chapter 4: Future of Edge AI with Wi-Fi Technology

Our new report, AI at the Edge: Technology Accelerated by Wi-Fi Services, dives deeply into the convergence of Wi-Fi services and Edge AI and explores how a technology model like WaaS can accelerate the adoption of AI at the edge.&nbsp;Below is an excerpt from Chapter 3 of the report. To read the full report, including in-depth case studies, download it now.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwNTE0NjU1NDU5LVJlbGF5IDIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;</iframe>The applicability of Wi-Fi Edge AI is fairly expansive and is growing by the day, spanning various industries and application areas. The most central aspects of this intersection of technologies are the real-time insights they provide, how these insights impact the speed and type of response, and how they are communicated across the network. Whether it is in smart healthcare, education, retail, or other application areas, Wi-Fi-enabled Edge AI is redefining how technology is leveraged in real time. Relay2 utilizes several Edge AI platforms to enhance the functionality of AI applications on the edge. In this chapter, we will take a look at some interesting use cases of Wi-Fi Edge AI, and we will see the impact of this technology convergence and the new opportunities it is opening in new and emergent technological spaces.<h2>Education / Smart Campus</h2>Wi-Fi Edge AI plays an important role in education-related applications, among which is remote learning integration, facilitating seamless and interactive remote education and bridging the gap between on-site and remote learning experiences. With localized content engagement pattern analysis and personalized experiences for students, Wi-Fi services and edge computing can help enhance and refine education content delivery.&nbsp;Campus facilities can also benefit significantly from integrating such technologies, especially in terms of security and surveillance, where Wi-Fi Edge AI can help analyze surveillance videos locally and in real time to recommend timely actions for security staff. It also powers use cases like attendance automation and traffic patterns, streamlining attendance tracking for educators and student flow analysis for operation enhancement.&nbsp;Another use case of Wi-Fi and edge computing can be seen in revenue generation, in which they enable intelligent, location-based advertising through audience behavior analysis for commercial partners. By leveraging these technologies, educational institutions can help promote campus services better, boost product marketing across campus premises and beyond, and enable efficient digital transactions.One example of applying Wi-Fi and edge computing in education is by WENet, an OEM partner of Relay2 in Vietnam. In order to support the University of Transport Technology in Vietnam in developing a digital technology infrastructure platform, WENet deployed Relay2’s Wi-Fi-enabled edge computing solution, making use of the advantages provided by Relay2 ServiceEdge Platform compared to conventional Wi-Fi networks. From stable bandwidth to fast processing speed, short deployment time, minimal service interruption time, and optimal operating costs, the features brought about by Relay2’s ServiceEdge Platform were highly effective. As a result, over 17 thousand students across three campuses were able to benefit from this platform, especially in terms of the following three key functions:<ul><li>Digital signage interacting with mobile devices to support two-way communication (university-to-student communication and student responses to university requests)</li><li>Students’ access to digital education materials from Relay2’s Service Points</li><li>Students’ ability to order food digitally from the cafeteria</li></ul>Such functions have not only enabled students to benefit from a Wi-Fi-powered edge computing platform but also helped collect analytic data for future AI processing at the edge. Read more about the WENet use case on the Relay2 website.<h2>Smart Healthcare</h2>Undoubtedly, one of the most important aspects of healthcare is real-time monitoring and real-time decision-making. However, as we have seen over the recent years with pandemic-caused crises, healthcare workers are more often than not overworked, and hospitals are resource-limited, which can limit their abilities to provide timely care. In order to ensure patient safety and health within hospital spaces and elsewhere, it is crucial to track, monitor, analyze, and act in real-time. All this can be made available by incorporating Edge AI and Wi-Fi. Whether it is remote monitoring and management, patient activity tracking, patient care, or even assistance during healthcare crises, hospitals, and medical personnel can benefit significantly from integrating Wi-Fi Edge AI into their processes.Smart healthcare comes in the form of connected healthcare, where AI-based smart devices, healthcare management software, and digital instruments are integrated into a “connected” healthcare system using a network protocol like Wi-Fi. As such, medical personnel would have access to all the medical-related information they need to provide the patients with the necessary medical treatment in the most effective and convenient way possible. This has been made possible by utilizing Edge AI and Wi-Fi technologies to empower the Internet of Medical Things (IoMT). In other words, healthcare personnel can leverage the low-latency capabilities of Edge AI processing and the ubiquity of Wi-Fi connectivity to access real-time data from diverse medical sensors, enabling timely decision-making and interventions and fostering a proactive approach to patient care.Consider intensive care units (ICUs), operating rooms, or other healthcare spaces, the use of Edge AI and Wi-Fi can augment the work of doctors, nurses, and clinicians by minimizing limitations, redundancies, and time allocated to activities away from direct patient care (such as documentation). As a result, medical workers can benefit from an emerging ambient intelligence , powered by edge computing, AI capabilities, and Wi-Fi. This can even be expanded to daily living spaces, which would support independent living, chronic disease management, mental health, and physical rehabilitation, particularly of elderly individuals.<h2>Smart Retail</h2>Global retail is almost always in flux, and both retailers and suppliers are grappling with the complexities of ever-changing consumer demand, behavior, and expectations. The continuous shifts in these dynamics create uncertainties in predicting future trends and exert significant stress on international supply chains and store operations. Navigating these challenges necessitates real-time adaptability to stay ahead in the retail industry.Edge AI emerges as a potent solution, offering retailers the capability to swiftly and precisely forecast daily demand while automating crucial aspects of supply chain logistics, customer engagement, and operational agility. This not only addresses challenges related to in-store supply management but also tackles issues in last-mile delivery costs, aligning with consumer expectations regarding timely deliveries.Within physical stores, retailers leverage Edge AI to combat issues such as shrinkage and stockouts, optimizing inventory management. Simultaneously, the application of Edge AI and Wi-Fi contributes to the creation of frictionless in-store experiences while prioritizing the health and safety of both employees and shoppers. The integration of AI in retail is thus transforming operations, offering a multifaceted approach to address the intricate challenges posed by the dynamic retail landscape.D.Phone, one of the largest cell phone retailers out of China, has deployed in-store Wi-Fi to enhance its customer experience and promote engagement as a way to offer a differentiated experience that would distinguish its &nbsp;D.Phone stores from competitors. They opted for Relay2’s wireless broadband solution, taking advantage of its unified native-cloud wireless networking and edge-service delivery.By leveraging Relay2 access points, D.Phone was able to offer high-quality customer experiences and deliver value-added services in-store. With its fast and reliable coverage in demanding environments like customer-dense spaces, including shopping centers and large venues, Relay2 access points enabled D.Phone to meet its customers' expectations and offer them the high-speed connectivity they desire. Moreover, with Relay2’s Edge services, D.Phone was able to run sales-generating applications and computation-intensive services locally and place content right at its &nbsp;D.Phone customers’ fingertips, thus reducing content download time to just seconds. This helped streamline the customer purchasing experience and circumvent bandwidth bottlenecks. It even enabled D.Phone to deliver new services to its customers, including app delivery and couponing. This has displayed an accelerated go-to-market process for D.Phone’s digital merchandise and a reinforced branding strategy. Read more about the D.Phone use case on Relay2’s website.<blockquote>“Relay2’s plug-and-play access points and cloud-based platform [have] given us a solution that lowers the cost of deployment and management.” - Wu Huan, CIO, D.Phone</blockquote><h2>Smart Buildings</h2>With the recent growth in smart homes and building automation systems, edge computing, AI, and Wi-Fi have become increasingly popular in this space. The integration of Edge AI with sustainable architecture and smart buildings has been rapidly gaining momentum and transforming the manner in which we construct and interact with our built spaces. With advantages like enhanced energy efficiency, optimized systems, personalized occupant comfort, and streamlined building maintenance, smart buildings are leveraging the real-time analysis capabilities of Edge AI and its integration with IoT and developing potential for more sustainable architecture and digital integration.Acting as a centralized intelligence hub, AI processes and interprets data obtained from an array of edge devices (IoT devices) such as sensors and actuators scattered throughout the building. This network of devices forms a comprehensive information system, providing valuable insights into various aspects of the building's operations. Through the implementation of AI algorithms, smart buildings gain the ability to efficiently monitor and regulate crucial functions like lighting, heating, ventilation, air conditioning, security, and energy consumption. Such an integration results in an environment that is not only responsive but also adaptive, catering to the dynamic needs and preferences of the building occupants.This can be further supported by Wi-Fi connectivity and cloud-based Wi-Fi services, in particular. Cloud-based Wi-Fi services enable centralized management and control of Wi-Fi networks. This centralized approach simplifies the deployment and maintenance of Wi-Fi connectivity across the building, ensuring a more streamlined and scalable infrastructure. Furthermore, the cloud facilitates the storage and analysis of the vast amounts of data generated by IoT devices in Smart Buildings. AI algorithms require substantial data inputs to make informed decisions, and cloud-based services offer the computational resources needed for processing and interpreting this data. It enables more sophisticated analytics, allowing for better insights into building operations, occupant behaviors, and energy consumption patterns.&nbsp;Similarly, the cloud's scalability can easily accommodate the dynamic nature of Smart Buildings, especially when the number of connected devices grows or changes. Just as importantly, cloud-based services enable remote monitoring, management, and updates of Wi-Fi networks and connected devices. This remote accessibility is invaluable for building administrators, allowing them to troubleshoot issues, implement updates, and optimize network performance without physically being on-site. In this regard, smart buildings can benefit meaningfully from Relay2’s innovative solutions. With advanced networking capabilities, Wi-Fi Edge Computing power, and intelligent analytics, users of Relay2’s ServiceEdge platform can ensure seamless connectivity and optimized operational efficiency.<h2>Smart Agriculture</h2>The Internet of Things (IoT) and edge computing have influenced the agricultural and farming industries quite extensively over the past decade, especially with the rapid rise of low-power wide-area networks (LPWAN) . Wireless communication protocols are, in fact, a vital part of smart agriculture systems, especially since the fields cover large areas that necessitate system components to be placed at remote locations. The integration of Wi-Fi and edge computing has been key to expanding the digital functionalities in agriculture, especially with the advent of AI at the edge, thus increasing the possibilities of smart agriculture over large areas.One type of smart agriculture that has been propelled by Edge AI and Wi-Fi services is precision farming. By deploying sensors and cameras in fields, farmers can gather real-time data on crucial factors such as weather patterns, soil conditions, crop growth, and pest infestations. This localized data processing enables farmers to modify and regulate their irrigation, fertilization, and pest control without delay, leading to an increase in crop yields and resource efficiency. The implementation of edge devices equipped with sensors extends beyond crop monitoring to include livestock health and behavior. Wearable devices on animals, such as cattle, provide early indications of illness or oestrus, empowering farmers to take timely actions, resulting in improved animal well-being, reduced rates of mortality, and higher farm productivity all around.&nbsp;Furthermore, the incorporation of Edge AI has paved the way for autonomous farming machinery, where tractors and drones equipped with edge devices can analyze field data in real time. This enables them to refine their routes, control planting depths, and identify and eliminate weeds in an autonomous manner. Not only does autonomous farming machinery reduce labor costs, but it also minimizes the use of chemicals, aligning agriculture with more eco-friendly practices. This is further empowered with the integration of Wi-Fi Services, allowing for centralized, remote monitoring and seamless connectivity between edge devices across large fields and in remote areas. The seamless integration of Edge AI and Wi-Fi technologies is proving transformative in making agriculture smarter, more efficient, and more sustainable over expansive agricultural landscapes.Relay2 offers smart farms state-of-the-art solutions that can help transform crop production, efficiency, and sustainability. These solutions seamlessly connect and monitor agricultural devices, collect real-time data, and leverage advanced analytics for informed decision-making. By optimizing resources, automating processes, and implementing precision farming, Relay2 empowers farmers to maximize yield and sustainability.<hr>This was an excerpt from Chapter 3 of the AI at the Edge: Technology Accelerated by Wi-Fi Services report. If you would like to read the full report, including in-depth case studies, you can download it below. &nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwNTE0ODA2OTE3LVJlbGF5IDIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;</iframe><h3 dir="ltr" id="isPasted">Read excerpts of the other report chapters:</h3>Chapter 1: <a href="https://www.wevolver.com/article/ai-at-the-edge-report-chapter-1-wi-fi-analytics-services-enabling-edge-ai" rel="noopener noreferrer" target="_blank">Wi-Fi Services Enabling Edge AI</a>Chapter 2: <a href="https://www.wevolver.com/article/ai-at-the-edge-report-chapter-2-relay2-and-edge-ai" rel="noopener noreferrer" target="_blank">Relay2 and Edge AI</a>&nbsp;Chapter 4: <a href="https://www.wevolver.com/article/ai-at-the-edge-report-chapter-4-future-of-edge-ai-with-wi-fi-technology" rel="noopener noreferrer" target="_blank">Future of Edge AI with Wi-Fi Technology</a>&nbsp;

Wi-Fi-enabled Edge AI is redefining how technology is leveraged in real time.

AI at the Edge Report. Chapter 3: Applications & Case Studies

BrainChip Holdings Ltd is a company that specializes in neuromorphic computing. They're known for their innovative approach and strategic vision in the field of artificial intelligence (AI). Their technology aims to redefine how AI is implemented at the edge. This article shares the highlights from their latest investor podcast to provide a holistic view of the company's achievements, strategies, and future direction.<h4 dir="ltr">A Vision for the Future</h4>BrainChip is committed to advancing neuromorphic computing, a cutting-edge field that mimics the neural architecture of the human brain. This approach promises unprecedented efficiency and performance in AI applications, particularly in edge computing scenarios where power and speed are critical.&nbsp;The recent addition of Dr. Tony Lewis to the BrainChip leadership team underscores this commitment. With a rich background in AI, engineering, and neuromorphic technologies, Dr. Lewis brings a wealth of knowledge and a clear vision for the company's future.n this journey. With an illustrious career spanning AI, engineering, and neuromorphic technologies, Dr. Lewis's expertise is set to steer BrainChip towards new horizons.The announcement of Dr. Lewis joining the team heralds a new era for BrainChip, emphasizing the company's dedication to leading the edge in AI technology. Dr. Lewis brings a profound understanding of brain-inspired computing and robotics, with a track record of leading large research groups and pioneering innovations in the field. His appointment is not just a testament to BrainChip's current leadership in neuromorphic computing but also a clear indicator of its ambitious roadmap ahead.Dr. Lewis's background is particularly noteworthy, with a PhD in brain-inspired computing in robotics, positioning him as a perfect fit for BrainChip's mission. His career has been a journey towards this role, marked by significant achievements in neomorphic and robotics research funded by the US government and industrial partners. His experience spans across defense, space, biomedical, environmental monitoring, mobile phones, and personal computers, highlighting a versatile and impactful career.At Qualcomm, Dr. Lewis led the largest research project outside of wireless in corporate R&amp;D, focusing on brain-inspired computing. His foresight into the potential of deep learning and language models has been pivotal, anticipating the significant impact these technologies would have on the industry. His role at HP as the Global Head of the AI and Emerging Compute Lab further honed his skills, preparing him for the challenges and opportunities of being BrainChip's CTO.Dr. Lewis's decision to join BrainChip was driven by the recognition of AI's golden age and the critical role of edge computing in shaping the future. He acknowledges the challenges of cloud processing, including its massive energy consumption and latency issues, underscoring the necessity for efficient, edge-based solutions. BrainChip, with its focus on neuromorphic computing, represents the cutting edge of this transition, offering solutions that are not only technologically advanced but also environmentally conscious and cost-effective.<h4 dir="ltr">Strategic Market Position and Competitive Edge</h4>BrainChip's belief in AI as a significant economic driver is matched by its strategic focus on edge computing. The company's neuromorphic computing platform offers a compelling alternative to traditional AI solutions, providing extreme efficiency, low power consumption, and real-time processing capabilities. This positions BrainChip uniquely in a market that is increasingly moving towards decentralized, edge-based computing solutions.The competitive landscape of AI and neuromorphic computing is diverse, with several players vying for dominance. However, BrainChip sets itself apart through its comprehensive platform that supports a wide range of AI models and applications. The company's IP business model, which includes licensing fees and royalties, offers flexibility and scalability to partners and clients, further solidifying its market position.<h4 dir="ltr">Innovations and Achievements</h4>One of the key highlights from the investor podcast was BrainChip's focus on continuous innovation and product development. The introduction of the AKD1000 processor and the development of the next-generation IP are testament to the company's commitment to advancing neuromorphic computing. These technologies enable a broader range of use cases, from industrial automation to consumer electronics, by supporting more complex AI models and providing easier integration with existing workflows.BrainChip's success is also reflected in its strategic partnerships and collaborations. The company has established relationships with industry leaders, research institutions, and technology developers to enhance its product offerings and expand its market reach. These partnerships not only validate BrainChip's technology but also provide valuable feedback and insights that drive further innovation.<h4 dir="ltr">Looking Ahead: Challenges and Opportunities</h4>As BrainChip continues to navigate the complex landscape of AI and neuromorphic computing, it faces both challenges and opportunities. The fragmented nature of the edge AI market presents a significant challenge, requiring a nuanced understanding of customer needs and industry trends. However, BrainChip's flexible business model and customer-focused approach position it well to adapt and thrive in this dynamic environment.The potential competitive threat from major players like Nvidia moving into the edge computing space underscores the importance of maintaining a competitive edge through innovation and strategic partnerships. BrainChip's commitment to supporting standard machine learning workflows and its unique on-chip learning capabilities provide a strong foundation for sustaining its market leadership.<h4 dir="ltr">Conclusion</h4>BrainChip's journey in the AI industry is marked by bold visions, strategic innovations, and a relentless pursuit of excellence. The company's focus on neuromorphic computing not only sets it apart from competitors but also positions it as a pioneer in the future of AI. With a strong leadership team, strategic partnerships, and a commitment to continuous innovation, BrainChip is poised to lead the generational shift towards efficient, powerful AI at the edge. As the company navigates the challenges and opportunities ahead, its contributions to the field of AI and neuromorphic computing will undoubtedly continue to shape the technological landscape for years to come.

Highlights from BrainChip's latest investor podcast provide a view of the company's achievements, strategies, and future direction.

BrainChip Podcast: Shareholder and innovation update

BrainChip Holdings Ltd. has recently announced that it has opened pre-orders for the <a href="https://shop.brainchipinc.com/products/akida%E2%84%A2-edge-ai-box" target="_blank" id="isPasted">Akida Edge AI Box,</a>&nbsp; a significant milestone in the field of Edge AI. Through collaboration with VVDN Technologies, BrainChip has made significant progress in developing high-performance and energy-efficient artificial intelligence solutions that can be applied to various industries and use cases.<h4 dir="ltr" id="isPasted">Unveiling the Power of the Akida Edge AI Box</h4>The Akida Edge AI Box is engineered to harness the potential of neuromorphic computing, an innovative approach that mimics the human brain's neural networks. This technology enables the device to perform complex cognitive tasks more efficiently, using significantly less power than traditional computing methods.Designed for versatility, the<a href="https://shop.brainchipinc.com/" target="_blank">&nbsp;Akida Edge AI Box&nbsp;</a>finds its application across various sectors, including retail, security, smart cities, automotive, and industrial processes. It aims to offer a solution that not only reduces operational costs but also enhances data privacy and security by processing data on the device, thereby minimising the need for constant cloud connectivity.<h4 dir="ltr">Key Features and Applications</h4><ul><li dir="ltr">Ultra-Low Power Consumption: The device operates on minimal power, making it ideal for deployment in energy-sensitive environments.</li><li dir="ltr">High-Performance AI Capabilities: With the ability to handle high-level computations and process vast amounts of data, the Akida Edge AI Box is perfect for complex applications like image processing and real-time analytics.</li><li dir="ltr">Adaptability and Learning: Mimicking the plasticity of the human brain, the device can learn from new information and adjust its processing accordingly, enhancing its efficiency over time.</li><li dir="ltr">Real-Time Processing: The Akida Edge AI Box excels in scenarios requiring immediate data analysis and decision-making, such as autonomous vehicles and smart surveillance systems.</li><li dir="ltr">Robust and Resilient: Engineered to be fault-tolerant, the device ensures reliable operation even in challenging environmental conditions.</li></ul><h2 dir="ltr">Technical Specifications</h2><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1709550646582-Screenshot+2024-03-04+121015.png" style="width: 100%px;" class="fr-fic fr-dib">Priced at $799, the Akida Edge AI Box not only stands out for its technological prowess but also for its affordability, making advanced neuromorphic computing accessible to a broader range of industries.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5NTUwNDI5NjI5LWJyYWluYm94LmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">The Akida is best in class compared to current market products. Image credit: BrainChip.<h1 dir="ltr" id="isPasted">Bringing Edge AI Everywhere: Application examples</h1><h3 dir="ltr">Security and Surveillance</h3>the Akida Edge AI Box's capabilities have the potential to significantly transform the way monitoring and threat detection are carried out..&nbsp;Its ability to process data locally, with ultra-low latency, ensures real-time analysis of video feeds, detecting anomalies, unauthorized access, or suspicious behaviors instantly. This rapid processing can significantly enhance security measures in both public and private spaces, offering a more robust response to potential threats.<h3 dir="ltr">Smart Factories</h3>For smart factories, the Akida Edge AI Box brings the potential for improved automation, predictive maintenance, and operational efficiency. By processing data on-site, it can monitor equipment health in real-time, predict failures before they occur, and optimize manufacturing processes. This not only reduces downtime but also extends the lifespan of machinery, leading to significant cost savings and increased production efficiency.<h3 dir="ltr">Smart Retail</h3>In smart retail environments, the Akida Edge AI Box can personalize the shopping experience through real-time analytics. It can manage inventory, optimize store layouts based on customer behavior analytics, and offer targeted promotions to customers. This level of personalization and efficiency can enhance customer satisfaction and loyalty, driving sales and improving retail operations.<h3 dir="ltr">Smart Cities</h3>The Akida Edge AI Box can play a pivotal role in developing smart city infrastructure, from traffic management and waste management to energy conservation. Its capacity for edge processing allows for the real-time analysis of data from various sensors across the city, enabling intelligent decision-making that can improve city services, reduce congestion, enhance public safety, and lower environmental impact.<h3 dir="ltr">Warehouses</h3>In warehouses, the Akida Edge AI Box can streamline operations by optimizing logistics, managing inventory in real-time, and automating the tracking and dispatching of goods. This not only improves operational efficiency but also reduces the likelihood of errors, ensuring that the right products are always in stock and delivered on time.By bringing AI processing capabilities directly to the edge, the Akida Edge AI Box enables these sectors to operate more efficiently, safely, and sustainably. Its introduction represents a significant step forward in the deployment of intelligent, secure, and customized devices and services for multi-sensor environments, catering to the specific needs of these diverse applications.<h2 dir="ltr">The Future of Edge Computing</h2>The collaboration between BrainChip and VVDN Technologies in developing the Akida Edge AI Box is a testament to the power of strategic partnerships in driving technological innovation. This device is set to redefine the landscape of Edge computing, ushering in a new era of intelligent, efficient, and autonomous systems.By addressing the critical challenges of energy consumption, processing power, and data privacy, the Akida Edge AI Box is poised to play a crucial role in the evolution of Edge AI. Its launch not only meets the current market demand for more efficient computing solutions but also lays the groundwork for the future of intelligent devices and systems across a multitude of sectors.As industries continue to seek smarter, more efficient technologies, the Akida Edge AI Box stands as a beacon of progress in the realm of Edge computing, promising a future where artificial intelligence is both profoundly intelligent and sustainably efficient.<a href="https://shop.brainchipinc.com/" target="_blank">Order the&nbsp;Akida Edge AI Box here.</a>

BrainChip Holdings Ltd. Initiates Pre-Orders for the Akida Edge AI Box, Elevating Edge AI Capabilities through a Strategic Alliance with VVDN Technologies

Akida Edge AI Box: Edge Computing with Neuromorphic Technology

<table style="width: 100%;"><tbody><tr><td style="width: 100%;">Catch up on our recent webinar: Powering Edge and Endpoint Applications: A deep dive into Reality AI Tools® from RenesasMohammed Dogar, Vice President, Head of Global Business Development &amp; Ecosystem, and Jeff Sieracki, Director of AI COE, will sit with tech writer Samir Jaber to unpack the Reality AI Tools suite and discuss their impact on Edge AI applications. Why Watch?<ul><li dir="ltr">Learn: Understand the primary functions and benefits of using Reality AI Tools®</li><li dir="ltr">Connect: Hear from Mohammed Dogar, Vice President, Head of Global Business Development &amp; Ecosystem, and Jeff Sieracki, Director of AI COE</li><li dir="ltr">Engage: Opportunities to ask questions and interact with our experts</li></ul>Check it out here:<iframe width="640" height="360" src="https://www.youtube.com/embed/kjISoxQmpU0?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-42="true" id="691705897" data-gtm-yt-inspected-32="true" data-gtm-yt-inspected-43="true" data-gtm-yt-inspected-44="true">&nbsp;</iframe></td></tr></tbody></table>We are thrilled to share the 2024 State of Edge AI Report with you.&nbsp;This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.Report Highlights:<ul><li dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</li><li dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</li><li dir="ltr">#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.</li></ul>Download the full report below.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710954579366-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-42="true" data-gtm-yt-inspected-32="true" data-gtm-yt-inspected-43="true" data-gtm-yt-inspected-44="true">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</iframe><h2 dir="ltr">Report Introduction</h2>In the midst of all the technological advancements happening today, one term has taken over almost every discourse about technology: artificial intelligence (AI). Once a thrilling notion for Sci-Fi movies, AI is now an indisputable reality, and it has taken multiple industries by storm. From Large Language Models (LLMs) enabling fantastic chatbots like ChatGPT to Internet-of-Things (IoT) devices bringing about Industry 4.0, AI has found applications and use cases across almost every modern industry. While the majority of AI implementations have taken place centrally on cloud servers, there has been a strong pursuit for intelligence to be deployed locally at the edge.Artificial intelligence at the edge is referred to as Edge AI, and it is the convergence of AI model deployment with edge computing. By bringing computational power closer to the data source, Edge AI offers real-time data processing on edge devices with reduced latency, higher bandwidth efficiency, enhanced reliability, and more robust security and privacy. This has the potential to propel AI capabilities beyond its current centralized, cloud-based structure. As the adoption of Edge AI continues to grow almost exponentially, we will witness a paradigm shift in how businesses deploy and utilize AI, especially with edge computing decentralizing data processing.Industries that require real-time data processing capabilities are the primary industries in Edge AI’s scope of impact and are expected to experience a radical transformation as a result. Embracing this dramatic shift is essential for businesses that seek to stay ahead of the competition and leverage the benefits of real-time, context-aware decision-making. But knowing that Edge AI impacts every industry differently, how can a business in a specific industry embrace this technology to ensure it keeps pace with its rapid evolution?Accordingly, Wevolver has partnered with several major companies and thought leaders in this space to produce this holistic report on the state of Edge AI in 2024 and its applications in every relevant industry, from healthcare to industrial IoT and manufacturing, smart cities, retail, energy, agriculture, and automotive. Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples, complemented by featured sections of real-world case studies. The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today. Finally, it gives a glimpse into the future of Edge AI and how it will develop in the years to come.This is the second in-depth report on Edge AI by Wevolver after its <a href="https://www.wevolver.com/article/2023-edge-ai-technology-report" target="_blank">inaugural report in 2023</a>, which focused primarily on the technological side of Edge AI. This report explores Edge AI’s application side and its impact on the various industries mentioned above. Will Edge AI be one of the world’s fundamental technologies going forward? Will it continue to reshape our industries and become ubiquitous? If so, how? Let’s find the answers in the chapters below.Samir Jaber, Report Editor-in-Chief<h2 dir="ltr">About the report</h2>This technology report came to fruition thanks to the collaborative efforts of multiple contributors. Samir Jaber, the editor-in-chief, alongside John Soldatos and Ravi Rao, the supporting authors, have played integral roles in researching, developing, and editing the report content. The insights provided by our sponsors and contributors have significantly enriched the content. Special acknowledgment goes to Jessica Miley, the content director at Wevolver, for her consistent support and leadership throughout the process. Each contributor’s dedication and input are deeply valued, highlighting the collective effort that has gone into creating this report.<h3 dir="ltr">About the authors</h3>Samir JaberEditor-in-Chief Leipzig, Germany&nbsp;Samir Jaber is an editor, writer, and industry expert on technology, science, and engineering topics. He is an online content specialist with an academic background in mechanical engineering, nanotechnology, and scientific research. Samir has comprehensive experience working with major engineering and technology companies as a writer, editor, content manager, and digital marketing consultant. He is a featured author in 30+ industrial magazines with a focus on Artificial Intelligence (AI), the Internet of Things (IoT), 3D printing, Autonomous Vehicles (AV), nanotechnology, materials science, and sustainability. Samir is also an award-winning engineering researcher in the fields of nanofabrication and microfluidics.John SoldatosCo-author Athens, Greece&nbsp;Honorary Research Fellow at the University of Glasgow John Soldatos holds a Ph.D. in Electrical &amp; Computer Engineering from the National Technical University of Athens (2000) and is currently an Honorary Research Fellow at the University of Glasgow, UK (2014-present). He was Associate Professor and Head of the Internet of Things (IoT) Group at the Athens Information Technology (AIT), Greece (2006–2019), and Adjunct Professor at the Carnegie Mellon University, Pittsburgh, PA (2007–2010). He has significant experience working closely with large multi-national industries (e.g., IBM, INTRACOM, INTRASOFT International) as an R&amp;D consultant and delivery specialist while being a scientific advisor to various high-tech startup enterprises. Dr. Soldatos is an expert in Internet-of- Things (IoT) and Artificial Intelligence (AI) technologies and applications, including IoT/AI applications in smart cities, finance (Finance 4.0), and industry (Industry 4.0).&nbsp;Ravi RaoCo-author Ahmedabad, India&nbsp;Ravi is a software engineer at one of the world’s largest IT consulting firms. He works on digital transformation projects that help streamline operations for customers. Ravi’s education in Power Electronics Engineering and his professional experience have equipped him with expertise in electrical, electronics and computer engineering. Ravi has authored two IEEE research papers: one exploring the integration of AI and IoT in irrigation systems and another assessing the implementation of a curriculum to stimulate innovation among engineering students. Additionally, Ravi has earned a patent for a project aimed at enhancing independence for individuals with mobility challenges. Outside work, Ravi is passionate about writing on technology, gadgets, and gaming. He also loves engaging in conversations about the latest advancements in technology.<h2>About the Sponsors</h2>We want to acknowledge the support of our sponsors for their contribution to the State of Edge AI Report. The contributions provided were essential in facilitating the research and findings presented. Our sponsors' engagement reflects their commitment to the ongoing discussions and developments in the field of Edge AI.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712054291519-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%287%29.png" style="width: 100%px;" class="fr-fic fr-dib"><h2>Download your copy of the 2024 State of Edge AI Report now.&nbsp;</h2><iframe width="540" height="450" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-42="true" data-gtm-yt-inspected-32="true" data-gtm-yt-inspected-43="true" data-gtm-yt-inspected-44="true">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</iframe>This is Wevolver's second in-depth report on Edge AI after its <a href="https://www.wevolver.com/article/2023-edge-ai-technology-report" target="_blank">inaugural report in 2023</a>, which focused primarily on the technological side of Edge AI.&nbsp;

We are pleased to share Chapter 1 of the&nbsp;2024 State of Edge AI Report with you.&nbsp;This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.Report Highlights:<ul><li dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</li><li dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</li><li dir="ltr">#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.</li></ul>Download the full report below.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712055006106-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-42="true">&nbsp;&nbsp;</iframe><h2 dir="ltr">The Evolution of AI: From Research to Edge Technology</h2>Edge AI’s growing momentum over recent years has not been a stroke of luck or an unexpected turn of events. It’s been brewing in the research spheres for quite a while now. In fact, the way AI influences technology has been a discussion since the mid-1900s, ever since Alan Turing set the standards for the “thinking machine,” and Christopher Strachey wrote the first successful AI computer program, followed by Arthur Samuel of IBM pioneering machine learning. From then on, AI went on a long rollercoaster ride of rises and dips, going through hype cycles and significant interest and funding all the way down to not one but two “AI winters” before the turn of the millennium. Nonetheless, in recent years, especially going into the 2020s, AI has taken yet another leap, but this time, it has taken off.With major developments in machine learning and the advent of technologies like cloud computing, edge computing, and fog computing, AI has significantly benefited from the data processing capabilities brought about by these technologies. Cloud computing’s ability to process massive amounts of data simultaneously and edge computing’s ability to process data locally and in real time are both crucial for AI’s mass adoption. This helps AI make informed decisions in fractions of the time that it would take humans, thus improving processes and systems across different industries and creating new and optimal ways of working. As a result, AI has witnessed significant growth in adoption rate across various large organizations, reaching 42% in 2023, according to the <a href="https://noticias.ai/wp-content/uploads/2024/01/IBM-Global-AI-Adoption-Index-Report-Dec-2023.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">IBM Global AI Adoption Index 2023</a>, with as many as 40% of organizations actively exploring the use of AI in their business operations. AI has once again captured the spotlight — and this resurgence has brought about a new wave of possibilities and opportunities to explore.<blockquote>“Simplicity is key in the tech world: a solution is only as successful as it is widely accepted, adopted, and applied. That’s why Arduino’s mission is to democratize technologies like Edge AI, making it an accessible option for people with different backgrounds and in all industries to solve problems, create value, and grow.” &nbsp;– Fabio Violante, CEO of Arduino</blockquote>Today, a lot of data processing is being decentralized from large cloud data centers to smaller localized data centers and edge devices. This has enabled the emergence of Edge AI, which processes data at or near the source of data generation. Many organizations are deploying edge functionalities, resulting in energy-efficient, low-latency applications with real-time performance. Edge AI offers significant data protection and security benefits, making it an attractive proposition for organizations across sectors to use edge computing features for various use cases. In the following sections, we take a look at the Edge AI market to see how it’s been responding to the technology’s potential, and we explore how different industries are adopting Edge AI into their workflows and systems.<h3 dir="ltr" id="isPasted">Edge AI Market Landscape</h3>For a technology that is moving quickly on the Gartner <a href="https://www.gartner.com/en/articles/what-s-new-in-artificial-intelligence-from-the-2023-gartner-hype-cycle" target="_blank">hype cycle for AI</a>, Edge AI is constantly finding new use cases in various industries, and its adoption is expected to grow further and faster. In fact, Gartner analysts predict that <a href="https://www.gartner.com/en/documents/4850031" target="_blank">edge computing technologies</a> will gain traction and maturity in 2024, especially with the significant drop in the cost of developing and deploying edge systems thanks to technical innovation in this space. Such improvements in the technology have enabled the Edge AI market to witness remarkable growth over recent years. According to <a href="https://market.us/report/edge-ai-market/" target="_blank">Market.US</a> research, the global Edge AI market is expected to surpass the USD 140 billion mark by 2032, a considerable rise from just over USD 19.1 billion in 2023. That is a compound annual growth rate (CAGR) of almost 26% across nine years.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710785750352-240303_IMAGES-01.jpg" style="width: 769px;" class="fr-fic fr-dib">The Edge AI Market is expected to reach USD 143.6 Billion by 2032, an exponential rise from its 2023 value of USD 19.1 Billion (Credit: market.us)The growth of the Edge AI market reflects the increasing integration of technology into various aspects of modern life. With the proliferation of IoT devices across industries, from manufacturing to healthcare, vast volumes of data are being generated continuously. This data holds significant potential for insights and optimization, but traditional centralized processing methods often struggle to handle real-time data processing without having to deal with latency issues.Edge AI addresses this challenge by bringing AI and machine learning algorithms closer to where the data is generated, at the “edge” of the network. This localized approach allows for real-time processing and analysis, minimizing latency, reducing bandwidth usage, and enabling quicker decision-making. For instance, within the realm of autonomous vehicles, split-second responses to changing road conditions are crucial for safety. Edge AI enables these vehicles to process sensor data onboard, ensuring faster reaction times.Moreover, advancements in semiconductor technology have played a crucial role in enabling more powerful and energy-efficient edge computing devices. These devices are capable of handling complex AI algorithms while remaining efficient enough to operate in resource-constrained environments, such as remote industrial sites or within wearable devices.The rollout of 5G technology further amplifies the capabilities of Edge AI solutions. With its significantly enhanced connectivity and data transfer speeds, 5G facilitates seamless communication between edge devices and central systems, enabling faster data transmission and response times. This is particularly beneficial in scenarios such as healthcare monitoring, where timely analysis of patient data can have life-saving implications.In essence, the growth of the Edge AI market represents a response to the evolving demands of industries and engineers in a data-driven world. It's about leveraging innovation to optimize processes, enhance efficiency, and ultimately improve the way industries operate.<h3 dir="ltr" id="isPasted">Industry Adoption and Trends of Edge AI</h3>“Under the radar thus far, edge is set to become a ubiquitous lever of scale and reinvention as artificial intelligence (AI)—including generative AI—driven applications become pervasive in enterprise functions and operations.” This is what researchers at Accenture articulated in their 2023 study on <a href="https://www.accenture.com/content/dam/accenture/final/accenture-com/document-2/Accenture-Leading-With-Edge-Computing.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">Leading with Edge Computing</a>. This statement sums up the current state of Edge AI in a nutshell. With edge computing permeating a lot of industries and application areas, it is reaching a level of ubiquity that renders its implementation almost a necessity. In fact, Accenture’s survey uncovered that 83% of executives across multiple industries think that in order to stay competitive in the future, edge computing will be essential. And many are fearing missing out on all of Edge AI’s benefits if they do not act quickly and incorporate it into their workflows, products, and services.&nbsp;However, the adoption of Edge AI is not uniform. While some companies regard edge as a key differentiator to bring AI into their core business, others still struggle with fully leveraging the technology’s benefits, mostly due to them considering it a standalone technology and using it in ad-hoc projects. Adopting edge computing strategically and integrating it with existing cloud strategies has shown the best outcomes: Advanced users of edge are four times more likely to achieve accelerated innovation, nine times more likely to increase efficiency, and seven times more likely to reduce costs (Accenture, 2023).<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710785803768-240303_IMAGES-02.jpg" style="width: 100%px;" class="fr-fic fr-dib">Edge moves computing closer to users and devices at the edge of the network, where it is the closest possible to data sources (Credit: Accenture)Looking at Edge AI from the industry level, we see a similar distribution, with some industries already utilizing the technology almost ubiquitously and others still exploring its potential. The manufacturing industry seems to take the lion’s share of revenue with approximately 31% thanks to the integration of automation and real-time insights, capitalizing on Edge AI’s benefits in defect detection, reduced latency, real-time decision-making, cost efficiency, and data security. Following it is the automotive and transportation sector, especially with the recent ACES trends (autonomous driving, connectivity, electric vehicles, and shared mobility) leading the industry, all of which require Edge AI, albeit at varying levels. Furthermore, traffic management is seeing significant developments by integrating Edge AI into its sensors, cameras, and traffic management systems (TMS). While other industries like healthcare, retail, energy, and agriculture acknowledge the potential of Edge AI, they are yet to adopt it at the same level as manufacturing and automotive.That being said, Edge AI is seemingly showing clear patterns and trends that are influencing the future of data science and machine learning (DSML) across many industries. As <a href="https://zeenea.com/3-ai-trends-identified-by-gartner-to-look-out-for-in-2024/" target="_blank">Gartner</a> outlined in 2023, one trend that is leading the way is Edge AI as a promise of responsiveness. Edge AI promises quicker decision-making by executing AI algorithms locally, bypassing the need for the Cloud or remote data center connections. This reduces latency and enhances system responsiveness.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710785850995-240303_IMAGES-03.jpg" style="width: 100%px;" class="fr-fic fr-dib">How organizations have been using AI in the past two years (Credit: IBM)Converging AI and edge computing leads to more efficient and potentially energy-saving solutions. Gartner has forecasted that, by 2025, more than half of data analysis by deep neural networks will occur at the point of capture in an edge system, a significant increase from single-digit percentage points in 2021. This shows the significance of Edge AI in the years to come and how its implementation will continue to grow and penetrate various systems and workflows.<hr><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712055077687-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-42="true"></iframe>

test subtitle

Edge AI Market Analysis and Trends

We are thrilled to share Chapter 2 of the&nbsp;2024 State of Edge AI Report with you.&nbsp;This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.Report Highlights:<ul><li dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</li><li dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</li><li dir="ltr">#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.</li></ul>Download the full report below.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712055225143-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> <h2 dir="ltr">Healthcare and Medical Applications</h2>In recent years, the emergence of Edge AI has played a significant role in the digital transformation of the healthcare sector. With the shift of AI capabilities closer to the source of data generation, Edge AI can enable real-time, data-driven clinical decisions, enhance accuracy, and boost privacy and data protection in various healthcare applications where sensitive data are used. There are a multitude of Edge AI use cases in medical and clinical settings, including use cases that improve existing capabilities and others that enable functionalities that were hardly possible before the advent of AI at the edge. These capabilities fulfill different objectives and serve the needs of various stakeholders, as outlined in the Edge AI use cases presented below.<h3 dir="ltr">Real-Time Patient Monitoring</h3>Continuous patient monitoring is crucial to ensuring timely detection of warning signs and providing appropriate interventions. Edge AI plays a vital role in patient monitoring use cases by enabling real-time analysis of data collected from various data sources like wearable devices, sensors, and electronic health records (EHR). Based on data processing at the edge, patient monitoring systems can instantly identify anomalies and trigger appropriate actions, such as alerting healthcare providers or autonomously adjusting medication dosages. Hence, real-time patient monitoring powered by Edge AI can significantly improve patient outcomes and reduce the risk of adverse events.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710785926339-4.jpg" style="width: 100%px;" class="fr-fic fr-dib">Edge AI is transforming the medical industry by enabling digital diagnosis and remote patient monitoring (Credit: Jenny Wang, Seeed Studio)<table style="width: 100%;"><tbody><tr><td style="width: 100%;">CASE STUDY: IMAGIMBOB<h2 id="isPasted">A Fast, Cost-Effective, and Reliable Way to Add Smart Features to Healthcare Devices</h2>The healthcare industry has seemingly infinite vital signs and symptoms to track; finding ways to turn these signals into medically useful information can mean game-changing improvements for the industry and its patients. Edge AI offers an exciting opportunity to propel medical care and healthful living to an impactful new level. It is particularly suitable for the privacy requirements of this industry because data can be kept on the device instead of being sent to the cloud.Among those testing the way forward is Imagimob, a company intent on making it easier to deploy machine learning on edge devices. With their collection of off-the-shelf models that can be added to healthcare and wearable products with ease and an end-to-end development platform for solving problems with custom Edge AI models, Imagimob’s tools can change healthcare for the better.<h2>The Fastest Way to Launch Smart AI Features</h2>A major barrier to creating smarter medical devices is the typically extensive development process. With Imagimob’s Ready Models, AI features can easily be deployed onto existing products without the significant time, cost, or machine learning expertise required for custom development.&nbsp;Ready Models currently available for use in healthcare include audio-based models for coughing and snoring detection. And this is just the beginning — additional Ready Models under development include models based on Radar, IMU, and Capacitive Sensing, which can be used for presence detection, fall identification, and more. &nbsp;<h3>Developing Sharper Models in Real Time&nbsp;</h3>Imagimob’s machine learning development platform, Imagimob Studio, covers the entire workflow — from data collection to quick deployment in a healthcare product. Their recently launched Graph UX interface helps produce even higher quality models by letting engineers see them working in real-time.Take, for instance, a model that identifies coughing in a healthcare setting. In a scenario where coughs are being under-identified, an engineer can pinpoint which data the model is failing to classify and make direct improvements.&nbsp;“Graph UX makes models more robust as you have greater visibility and can identify problems fast,” says Alexander Samuelsson, Imagimob’s CTO. “This is a great advantage for the healthcare industry, where the ability to adapt quickly to new scenarios and information can be critical.”The possibilities are limitless. Learn more about how Imagimob’s Edge AI solutions are helping to build the intelligent products of the future with applications in healthcare and beyond at <a href="http://www.imagimob.com" target="_blank">www.imagimob.com</a>.</td></tr></tbody></table><h2 dir="ltr">On-Device Medical Imaging and Smart Rehabilitation</h2>Traditional medical imaging techniques require transferring large amounts of data to remote servers for processing, which creates concerns about latency and privacy. With Edge AI, medical imaging can be performed directly on the device, such as ultrasound machines and MRI (Magnetic Resonance Imaging) scanners. This ensures enhanced accuracy and reduced diagnosis time while increasing data protection. In particular, Edge AI algorithms can detect abnormalities in scans and provide immediate feedback to radiologists, enabling more efficient diagnoses. Furthermore, Edge AI addresses privacy concerns by minimizing the transmission of sensitive patient data over the network.Technology trends like TinyML, embedded machine learning, and on-device neuromorphic computing are likely to increase the number and variety of embedded medical imaging applications in the years to come. On-device inference for medical decision-making is set to become smaller yet faster, smarter, and more privacy-friendly.Similarly, rehabilitation processes can benefit significantly from real-time feedback in order to optimize therapy routines and improve patient outcomes. Edge AI empowers smart rehabilitation devices with on-device inference, allowing them to analyze sensor data and provide real-time feedback to patients during rehabilitation sessions. For instance, an Edge-AI-powered prosthetic limb could adjust its movements based on the patient's gait analysis toward a more natural and personalized experience. Based on Edge AI, rehabilitation devices can adapt to individual needs in real time, which enhances the effectiveness of therapies and reduces the need for constant supervision.<h3 dir="ltr">Clinical Trials with Real-World Data</h3>Real-time medical adherence monitoring is essential in clinical trials to ensure accurate data collection and protocol compliance. Edge AI enables the integration of real-time monitoring devices that track medication intake, adherence to treatment plans, and patient vitals. By leveraging Edge AI algorithms, researchers can assess, in real time, whether participants are following the prescribed protocols.&nbsp;Such functionalities can provide valuable insights into the effectiveness of treatments while reducing the burden of manual data collection. Most importantly, they are key to ensuring that clinical trials adhere to the prescribed protocols, which increases their credibility. Edge AI on real-world data is, therefore, a technology that will be increasingly adopted and used by Contract Research Organizations (CROs) in a variety of clinical trials worldwide.<h3 dir="ltr">Prediction, Detection, and Tracking of Disease Outbreaks</h3>Processing local data at the edge is particularly valuable for predicting and tracking disease outbreaks. Based on the analysis of medical records, wearables data, and environmental factors in real-time at the edge, healthcare organizations can detect early signs of potential outbreaks. This is fundamental to enabling proactive measures to prevent their spread. Edge AI can also ensure privacy and data protection by minimizing the reliance on centralized data repositories, which mitigates the risk of unauthorized access to sensitive healthcare information.Many healthcare applications can also benefit from decentralized learning models at the edge, such as federated learning. Federated learning is a technique in which global models are developed by combining locally trained models. It is already used in various disease detection and prediction use cases to enhance accuracy. The latter stems from the fact that global models turn out to be more accurate than local models, as they are, in principle, trained on more data.&nbsp;In the scope of a federated learning approach, edge devices (e.g., smartphones or IoT sensors) locally train AI models using data from individual patients or individual care service providers (e.g., care centers). Encrypted model updates are then sent to a central server, where they are combined to create a global model without compromising the privacy of individual data. This approach has been successfully applied by organizations like the World Health Organization (WHO) during the COVID-19 pandemic, where local models trained on edge devices contribute to a comprehensive global model that aided in disease detection and prediction.Decentralized learning paradigms will be increasingly adopted as the number of connected medical devices and other healthcare data sources grows rapidly. Managing huge numbers of data sources in a centralized manner increases data protection risks and creates latency and power efficiency concerns. This is why it has to be avoided whenever possible and substituted by decentralized systems powered by Edge AI.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710786074822-240303_IMAGES-04.jpg" style="width: 100%px;" class="fr-fic fr-dib">Artificial intelligence-enabled remote patient monitoring architectures (Credit: Shaik, T. et al., WIREs) Overall, Edge AI applications in healthcare offer numerous benefits, ranging from real-time patient monitoring and on-device medical imaging to clinical trials with real-world data and smart rehabilitation devices. Based on Edge AI paradigms, healthcare organizations can achieve real-time performance, enhanced accuracy, and improved patient outcomes. Moreover, privacy and data protection are prioritized due to the reduced transmission of sensitive information over networks, minimizing the attack surface of the healthcare application. In the coming years, Edge AI technologies will empower healthcare professionals to work faster and more effectively, delivering tangible benefits to millions of patients worldwide.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712055297468-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>

test test subtitle 

Healthcare and Medical Applications

Industrial IoT and Manufacturing

Smart Cities and Urban Infrastructure

We are pleased to share chapter 5 of the 2024 State of Edge AI Report with you.&nbsp;This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.Report Highlights:<ul><li dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</li><li dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</li><li dir="ltr">#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.</li></ul>Download the full report below.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712058814314-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> <h1 id="isPasted">Retail and Customer Experience</h1>The retail sector is undergoing a significant transformation driven by Edge AI, responding to mounting margin pressures and evolving consumer expectations. According to a <a href="https://www.mckinsey.com/industries/retail/our-insights/automation-in-retail-an-executive-overview-for-getting-ready" target="_blank">report</a> by McKinsey &amp; Company, retailers, from grocery to specialty stores, face margin pressures ranging from 100 to 500 basis points, highlighting a critical need for efficiency gains.Edge AI offers a solution to improve margins by 300 to 500 basis points through technologies like self-checkout systems and digital shelves. By leveraging the capabilities of Edge AI, retailers are not only addressing the immediate challenges of margin pressure but also getting ready for a future where technology-driven solutions redefine the shopping experience.<blockquote>“Edge AI transforms retail from transformative to experiential, revolutionizing customer interaction. It’s not merely about selling products; it’s about crafting personalized journeys that engage shoppers, fostering loyalty and driving growth.” – Samir Jaber, Editor-in-Chief</blockquote><h3 dir="ltr" id="isPasted">Inventory Management Perfected</h3>Efficient inventory management is a balancing act of critical importance. Retailers are constantly grappling with the twin challenges of stockouts and overstock — each carrying its own set of repercussions.&nbsp;Stockouts can result in lost sales and erode customer trust and satisfaction, leading to potential long-term implications on brand loyalty. Conversely, overstock situations tie up capital, increase storage costs, and lead to wastage, especially for perishable goods. These inventory misalignments directly impact a retailer's revenue and customer experience, making effective inventory management a foundation of successful retail operations.In response to these challenges, the integration of Edge AI in inventory management has emerged as a transformative solution. For instance, smart shelves equipped with weight sensors and RFID tags can automatically monitor stock levels. These shelves relay real-time data to Edge AI systems, which then analyze and predict stock requirements, minimizing the occurrences of stockouts and overstock. Another example is the use of AI-powered cameras that track inventory movement, providing instant data on stock levels and customer preferences.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710789963040-14.jpg" style="width: 100%px;" class="fr-fic fr-dib">Shelves with digital displays for price tags (Credit: MediaTile) Edge AI also enables retailers to seamlessly integrate online and offline retail experiences. This technological advancement hinges on Edge AI's capability to provide real-time updates on inventory levels, ensuring accurate and current information is available to customers regardless of their shopping channel. Such real-time synchronization is essential in today's omni-channel retail landscape, where a smooth transition between online browsing and in-store purchasing is expected.&nbsp;For instance, advanced predictive analytics and machine learning algorithms are employed to anticipate stock requirements, while digital price tags updated via Edge AI systems maintain price consistency across platforms. By utilizing these Edge AI-driven solutions, retailers effectively reduce the occurrence of stockouts and overstock, enhancing inventory management efficiency and ensuring a seamless and satisfying shopping journey for the customer.<table style="width: 100%;"><tbody><tr><td style="width: 100%;">CASE STUDY: SONY<h2>Scaling E-Commerce with Vision-Based AI for Inventory Management and Automation</h2>Retail and e-commerce center around customer satisfaction, reflected through online product reviews and ratings. While scaling retail or e-commerce, hidden pitfalls like inefficient warehouse utilization and inaccurate inventory affect their seller rating by customers, siphon profits, and stifle growth.This poses a challenge for online retailers that sell the same goods on multiple websites. The importance of omnichannel for e-commerce means inventory needs to be accurately reflected across all sales channels, instantly. When it’s not, order cancellations or wrongly shipped items can decrease seller ratings, resulting in deprioritization of the vendor’s product listings, a loss of sales influence of up to 80%, or even vendor suspensions until issues are addressed.&nbsp;The e-commerce boom brought FrioCONNECT challenges: inventory was inaccurate, counts were tedious, and 2% of listings were oversold or incorrectly shipped.When 20% of items don’t have the potential for restocking, companies like FRIOCONNECT cover the sunk costs associated with the mistake. To manage inventory, the process of counting 100,000+ items and 5,000 SKUs took five employees 10 days to complete. Without the counts, overselling or sending wrong items became more frequent.Gaining real-time, high-precision inventory visibility, once a luxury, was now a strategic imperative. FrioCONNECT recognized that automation of cycle counts was necessary to ensure correct inventory was represented on sales channels and to support the customer experience.Partnering with system integrator AVALTON and Sony’s AITRIOS™ team, the PHOTOBAR solution was created, leveraging Sony’s AITRIOS platform and IMX500 intelligent image sensor. In 2022, FRIOCONNECT dispatched over 75,000 items and realized a net profit of nearly 5-8% per product. They determined, based on their 7,800 square foot Doylestown, PA warehouse, that 348 IMX500-equipped devices would be needed to cover 58 racks and 696 shelves.&nbsp; <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710790025273-15.jpg" style="width: 100%px;" class="fr-fic fr-dib">Sony’s vision-based AI for inventory management and automation (Credit: Sony)The solution automates cycle counting in real time, using edge devices to capture shelf images, process on-chip, and send only inventory-relevant data to an API for analysis and counting. Now, the 10-day-long process can be completed twice a day or as often as necessary.Empowered by vision-based inventory management, FrioCONNECT increased order picking by 20%, optimized existing space, and is poised to gain back a projected $31,200 in error-based lost revenue from 2022, breaking even on their deployment of vision AI within a year, now with plans to expand the use of the technology across remaining warehouses.</td></tr></tbody></table><h3 dir="ltr" id="isPasted">Customer Behavior Analysis: Personalization at Scale</h3>In today's retail landscape, personalization is increasingly vital, with customers seeking experiences that cater specifically to their preferences and behaviors. Edge AI is instrumental in this shift, enabling the collection and analysis of customer data in real time. Brands leverage AI engines to parse through data gathered from online browsing, past purchases, and in-store interactions, which allows for tailored product recommendations.&nbsp;This use of data creates personalized shopping experiences and targeted marketing strategies, enabling retailers to meet the growing consumer demand for personalization with greater precision and effectiveness.Beyond online personalization, Edge AI is transforming the in-store experience. Through the integration of interactive displays and the analysis of data from in-store customer interactions, Edge AI aids retailers in comprehensively understanding and catering to consumer preferences.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710790099638-16.jpg" style="width: 100%px;" class="fr-fic fr-dib">Smart carts can transform the shopping experience by providing customers with product recommendations, navigating them through the store, and facilitating seamless checkout processes (Credit: Caper). Retailers can employ smart cameras and sensors within the store to track and analyze customer movements and their interactions with products in real time. This rich stream of data informs decisions on product placement, store layout, and marketing strategies. By leveraging these insights, shops are empowered to optimize the environment, making it more engaging, tailored, and responsive to the unique behaviors and preferences of their customers.Implementing Edge AI in customer behavior analysis presents mutual benefits for retailers and consumers. For retailers, the enhanced understanding of customer preferences leads to increased sales and better inventory management. Consumers, on the other hand, enjoy a more personalized shopping experience with recommendations and offers that are relevant to their interests and needs. This synergy is often supported by industry data and case studies, showcasing significant improvements in customer satisfaction and loyalty.<table style="width: 100%;"><tbody><tr><td style="width: 100%;">CASE STUDY: QUALCOMM<h2>Qualcomm Technologies: The Power to Transform Retail with On-Device Generative AI</h2>On-device AI fuels a more capable, cost-efficient, reliable, private, secure, and promising path forward for retail. Capable of working in harmony with cloud AI, edge devices deliver a faster, more efficient, and highly optimized AI with computing power you can rely on.Qualcomm technologies are helping retailers stay competitive in the digital age by transforming retail with on-device, generative AI. Last year, the breakthroughs in generative AI were monumentally exciting. Aware of its vast potential for several years, Qualcomm Technologies, Inc. has made it so that AI is holistically engineered into our processors. This year, in-store and online shopping are expected to remain equally popular. As physical retail regains momentum, we see an opportunity to fulfill consumers’ expectations by enabling seamless and integrated in-store experiences.From on-device Generative AI applications for front-line workers and retail analytics to biometric Point-of-Sales (POS) payments and frictionless self-checkout and loss prevention, Qualcomm processors power many commercially available retail solutions and help deliver real results through:&nbsp;<ul><li dir="ltr">Generating insights for retailers’ decision-making</li><li dir="ltr">Boosting loss prevention to address the industry’s $112B “<a href="https://cdn.nrf.com/sites/default/files/2023-09/NRF_National_Retail_Security_Survey_2023.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">retail shrink</a>” problem</li><li dir="ltr">Automating manual tasks to augment the workforce and minimize error rates</li><li dir="ltr">Enhancing overall customer experience as consumers increasingly return to in-person shopping</li></ul>Our ecosystem of top-notch customers and collaborators is making these transformations a reality. Their products address many of the primary challenges retailers face in areas such as frictionless checkout, loss prevention, inventory management, and operational analytics while also enhancing the all-around shopper’s journey. <a href="https://www.qualcomm.com/news/onq/2024/01/the-power-to-transform-retail-with-on-device-generative-ai" target="_blank">Learn more</a> about how Qualcomm technologies are transforming retail in 2024 and explore further in <a href="https://www.qualcomm.com/content/dam/qcomm-martech/dm-assets/documents/qualcomm-demonstrations-from-nfr-2024-event.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">this infographic</a>.For more information, check out <a href="https://www.qualcomm.com/products/internet-of-things/industrial/retail" target="_blank">Qualcomm Technologies’ retail solutions</a>.&nbsp;</td></tr></tbody></table><h3 dir="ltr" id="isPasted">Checkout Automation for Reduced Waiting Times</h3>One of the perennial pain points in the retail experience is the checkout process. Traditional checkout methods are often plagued with long queues and transactional delays, detracting from customer satisfaction and efficiency. These inefficiencies not only frustrate customers but also impact the overall throughput of retail operations — Edge AI steps in as a game-changer, offering innovative solutions to streamline the checkout process.Examples of such innovations include automated checkout systems and smart carts. Automated checkout systems, which use computer vision and sensor fusion, are capable of identifying items in a cart and processing transactions without the need for manual scanning. This approach significantly speeds up the checkout process, enhancing customer satisfaction by reducing wait times. Smart carts, on the other hand, are equipped with barcode scanners and payment systems that allow customers to scan and pay for items as they shop, further reducing queue times.Another application of Edge AI in retail is through self-checkout kiosks. These kiosks utilize machine learning algorithms to quickly identify items, offer various payment options, and even provide personalized offers based on the customer's shopping history. This not only enhances the checkout experience but also reduces wait times, thereby improving overall customer satisfaction and efficiency in retail operations.By minimizing the time spent in queues and making transactions more seamless, retailers can ensure a more positive end-to-end shopping experience for their customers. Moreover, these technologies often lead to better resource allocation within stores, as employees freed from traditional checkout roles can focus on customer service and other value-added activities.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710790956909-17+small.jpg" style="width: 100%px;" class="fr-fic fr-dib">In addition to security and surveillance, cameras can play a crucial role in gathering valuable insights into customer behavior, enabling personalized shopping experiences.The integration of specific Edge AI devices and technologies in retail is not just a futuristic concept but a present reality. From smart shelves to checkout automation, these innovations are reshaping the retail landscape, offering high levels of efficiency, personalization, and customer satisfaction like never before. As we look ahead, the continued advancement and integration of these technologies promise to revolutionize the retail experience further, making it more adaptive, responsive, and customer-centric.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712058858679-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> Powered by <a href="https://www.froala.com/wysiwyg-editor?pb=1" title="Froala Editor">Froala Editor</a>

Retail and Customer Experience

We are pleased to share chapter 6 of the 2024 State of Edge AI Report with you.&nbsp;This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.Report Highlights:<ul><li dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</li><li dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</li><li dir="ltr">#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.</li></ul>Download the full report below.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712058922621-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> The global demand for energy is at an all-time high, driven by population growth and economic development. This relentless consumption exerts immense strain on our natural resources and contributes significantly to climate change. In response, improving energy efficiency and sustainability has become an absolute necessity.Initiatives such as COP28 and the global stocktake that took place in November 2023 highlight this urgency, establishing ambitious benchmarks for international efforts. The search for solutions must extend beyond traditional methodologies, necessitating innovative approaches.A notable example of such innovation is highlighted in a McKinsey report, which suggests that adopting AI-driven methods in grids could enhance energy efficiency by 2-5% at the very point of generation, with up to 10% improvement in production and 30% in cost savings. This shift towards Edge AI in areas such as energy consumption monitoring, renewable energy integration, and smart grid management marks the next logical step in harmonizing our energy needs with the planet's health.<blockquote>“Edge AI is a crucial technology in this world of finite resources. For example, it allows us to monitor and optimize electricity and water consumption in real time. Manufacturing, agriculture and logistics can thus minimize their impact, leading to huge cost savings and lowering the carbon footprint.” – Fabio Violante, CEO of Arduino</blockquote><h2>Smart Energy Monitoring for Consumer Awareness and Cost Savings</h2>Smart metering, bolstered by Edge AI, signifies a significant advancement in the way energy consumption is monitored. This combination not only facilitates real-time monitoring but also propels consumer engagement to new heights.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710791714038-18+small.jpg" style="width: 100%px;" class="fr-fic fr-dib">Edge-AI-powered smart energy meters revolutionize energy management by providing precise, real-time data on consumption.By providing detailed insights into energy usage patterns, consumers are empowered to make informed decisions, leading to more sustainable consumption habits. Edge AI plays a crucial role in this ecosystem by processing data on the spot, which helps quickly identify areas of excessive use or inefficiency.Building on enhanced consumer awareness, Edge AI extends its impact to the operational level, driving efficiency and sustainability in various sectors. Implementing Edge AI in energy management systems has proven to be a game-changer for cost savings. In sectors such as commercial real estate, Edge AI has been instrumental in optimizing (HVAC systems, resulting in significant energy and cost savings.Manufacturing is another area where Edge AI has made a substantial impact, streamlining energy consumption in line with production needs and thus lowering operational costs. These instances exemplify how Edge AI can foster consumer engagement and effectively reduce energy expenditure, aligning economic benefits with sustainability objectives.<h2>Innovations in Renewable Energy Integration Powered by Edge AI</h2>Renewable energy sources, such as solar and wind, are at the core of our efforts for a sustainable future. Edge AI enhances the integration of these renewable sources by optimizing their operation and efficiency. For solar energy systems, Edge AI algorithms adjust panel angles in real time to capture maximum sunlight. At the same time, such algorithms can optimize the blade rotation of wind turbines to harness wind energy more efficiently. The wind power generation maintenance market has also expanded, reaching approximately 35.7 billion RMB in China in 2022.&nbsp;Utilizing AI visual inspection, manufacturers have developed systems that ensure over 95% accuracy in detecting critical issues like ice accretion and cracks, significantly reducing maintenance costs and enhancing turbine efficiency. By ensuring more reliable and efficient wind power generation, these Edge AI systems support the integration of wind energy into microgrids, which are essential for decentralized energy systems. This not only contributes to the resilience and sustainability of energy supply but also accelerates the transition towards a more diversified and renewable energy mix, aligning with global environmental and energy security goals. This real-time data processing and decision-making capability of Edge AI ensures that renewable energy sources operate at their peak efficiency, significantly increasing their output and reliability.As Edge AI continues to refine the efficiency and output of renewable sources, it sets the stage for their increased adoption and integration into global energy systems. In countries leading in renewable energy, such as Germany and Denmark, Edge AI has played a pivotal role in managing the variability of renewable sources, ensuring a stable energy supply to the grid.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710791616131-19+small.jpg" style="width: 100%px;" class="fr-fic fr-dib">Integrating renewable energy poses challenges due to its variability and the need for advanced grid management to ensure reliability and stability.Similarly, in remote and emerging regions, Edge AI has facilitated the deployment of microgrids powered by renewable sources, providing reliable energy access to communities. &nbsp;The collaboration between Engie Energy Access and Atlas AI in Kenya serves as a compelling example of how AI can facilitate the deployment of microgrids powered by renewable sources, thus providing reliable energy access to remote and emerging regions. By leveraging Atlas AI's predictive analytics and high-resolution geospatial data, Engie was able to identify high-density areas with significant demand potential for off-grid solar solutions. This data-driven approach enabled Engie to target and expand their renewable energy solutions specifically to communities most in need, resulting in a 48% increase in monthly sales in a pilot program. The use of AI in this context not only optimized Engie's operational focus and product offerings but also significantly contributed to enhancing energy access in underserved areas. These examples underline the global applicability of Edge AI in enhancing the scalability and effectiveness of renewable energy, marking a step forward in the global pursuit of sustainability.<h2>Proactive Smart Grid Management through Edge AI Solutions</h2>Smart grid management represents a key area where Edge AI is making a significant impact, particularly in balancing supply and demand with greater precision. By leveraging real-time data analysis, Edge AI enables the grid to respond dynamically to changes in energy demand and supply conditions. This not only improves the reliability of the energy supply but also enhances the overall efficiency of the grid. For instance, during times of low demand, Edge AI can facilitate the storage of excess energy or manage its redistribution, optimizing grid operations.Another area where Edge AI demonstrates its potential is in predictive maintenance, where its capabilities let it identify potential system vulnerabilities before they escalate. This foresight allows for preemptive adjustments to the grid, improving its resilience against disruptions and ensuring a stable energy supply. Predictive maintenance, enabled by Edge AI, can significantly reduce downtime and operational costs, further enhancing grid reliability and efficiency.The discussion on smart grids just can't be completed without addressing the crucial integration and enhancement of electric vehicle (EV) charging infrastructure. The widespread adoption of EVs hinges heavily on a robust and intelligent charging network. In 2023, the European Commission passed a law mandating the expansion of the EV charging infrastructure to match the growth in EV adoption. For every battery-electric car registered in a member state, public charging stations must offer a power output of 1.3 kW. Furthermore, starting in 2025, there will be a requirement to install fast recharging stations with a minimum power of 150 kW every 60 km along the trans-European transport network (TEN-T), ensuring widespread and efficient charging options for EV users. Taking a look at another initiative, the government of India is focusing on improving the EV charging infrastructure in its capital. The plan involves setting up at least one charging station every 3 square kilometers, supporting the wider goal of electric vehicles making up 25 percent of all new vehicle registrations by the end of this year.&nbsp;In this regard, Edge AI presents a game-changing opportunity to revolutionize this very infrastructure. Imagine charging stations that predict peak usage, dynamically allocate power based on vehicle needs, and even integrate with renewable energy sources for a truly sustainable experience. Through AI algorithms crunching real-time data on station availability, battery health, and even weather patterns, waiting times can plummet while charging efficiency skyrockets.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710791660174-20.jpg" style="width: 100%px;" class="fr-fic fr-dib">Optimizing EV charging infrastructure with Edge AI is crucial for balancing grid demands and facilitating the transition to electric mobility (Credit: Ather Energy)Predictive maintenance becomes possible, preventing downtime and ensuring smooth operation. Furthermore, edge AI empowers drivers with features like personalized charging plans and real-time updates on station status, fostering a seamless and stress-free journey. This revolution extends beyond individual stations, optimizing grid stability by intelligently managing power demands across entire networks. By harnessing the power of edge AI, we can unlock a future where EV charging is not just convenient but efficient, sustainable, and personalized, truly paving the way for a cleaner and more electrified tomorrow.<h2>Transforming the Conventional Energy Sector with Edge AI</h2>Edge AI is redefining safety and efficiency in the conventional energy sector, as well. On drilling floors, Edge AI-equipped cameras enhance safety by monitoring hazardous conditions in real time, significantly reducing the risk of accidents. Leak detection, traditionally reliant on extensive sensor networks, is revolutionized through Edge AI, where a single camera can identify leaks and other anomalies, streamlining monitoring processes and reducing equipment needs. Furthermore, in transportation and refinery operations, Edge AI optimizes routes and processes, improving efficiency and reducing environmental impact.&nbsp;Illustrating the impact of Edge AI, Aramco, a leading hydrocarbon producer, has markedly improved its efficiency by adopting the technology. In its Khurais oil field, thousands of IoT sensors have enhanced oil well monitoring, cut power consumption by 18%, and reduced maintenance costs by 30%. The deployment of drones and wearable tech has also slashed inspection times by up to 90%. These applications of Edge AI not only promote operational safety and efficiency but also support the sector's shift towards more sustainable practices, highlighting the technology's potential to transform conventional energy production and management.Edge AI's transformative impact on the energy sector is undeniable. Beyond smart metering and cost reduction, it optimizes renewable integration and enhances grid efficiency. While challenges persist, Edge AI represents a pivotal shift towards a sustainable future. Through responsible development and collaborative efforts, we can harness its power to unlock a cleaner, greener, and more resilient energy ecosystem, where sustainability is not a dream but a tangible reality.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712058953000-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>

Energy Efficiency and Sustainability

We are pleased to share chapter 7 the 2024 State of Edge AI Report with you.&nbsp;This report represents months of hard work, research, and dedication to exploring the dynamic world of Edge AI applications across industries.Report Highlights:<ul><li dir="ltr">#1:&nbsp;Industry-specific insights–&nbsp;Each industry is covered by a dedicated chapter that provides industry-specific insights, descriptions, and examples. ,</li><li dir="ltr">#2: Real-life case studies – Get comprehensive insights from featured sections of real-world case studies.&nbsp;</li><li dir="ltr">#3: A look into the future - The report also provides a peek into the exciting generative AI technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.</li></ul>Download the full report below.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712059017322-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> <h2 dir="ltr">Agriculture and Food Production</h2>As the global population, <a href="https://ieeexplore.ieee.org/document/9505674/" target="_blank">projected</a> to surge to 8.5 billion by 2030 and 9.9 billion by 2050, demands more food, the agricultural sector faces the dual challenges of increasing production sustainably and ensuring food security. With the dire need to significantly ramp up production to meet this escalating demand, the adoption of Edge AI becomes not just advantageous but essential.&nbsp;Edge AI is central to transforming food production, enabling advancements in precise management of crop/livestock, efficient resource utilization, enhanced quality assurance and beyond, addressing a spectrum of challenges with innovative solutions.<blockquote>“Integrating Edge AI into agriculture is about leveraging technology to optimize resources, maximize yields, and ensure food security for a growing population.” – Samir Jaber, Editor-in-Chief</blockquote><h3 dir="ltr" id="isPasted">Improved Crop Monitoring and Analytics for Maximizing Yield</h3>Edge AI enhances agricultural productivity by enabling precise monitoring of crop health, growth patterns, and soil conditions. By processing data from an array of sensors—such as moisture, pH, and nutrient sensors — and high-resolution imaging technologies, Edge AI algorithms can identify early indicators of stress, disease, or nutrient imbalances in crops. This timely intervention allows for tailored management practices, such as targeted application of fertilizers or pesticides, leading to healthier crops and maximized yields.Edge-AI-based systems can go a step further by harnessing real-time data on soil moisture and nutrient levels to optimize irrigation schedules and nutrient application, ensuring crops receive precisely what they need for optimal growth. This targeted approach not only conserves valuable resources but is also crucial in drought-prone areas, potentially turning the tide between crop failure and a successful harvest.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710791847903-21.jpg" style="width: 100%px;" class="fr-fic fr-dib">. Edge-AI-powered drones are transforming agriculture by enabling precise aerial monitoring and data collection, significantly improving crop health analysis and the efficiency of resource application (Credit: CropWatch).Furthermore, Edge AI empowers precision farming to become more controlled and accurate. Utilizing data-driven strategies, such as targeted irrigation and fertilization tailored to the unique needs of each plant, Edge AI significantly improves crop performance while reducing environmental footprints. <a href="https://ieeexplore.ieee.org/document/8453327" target="_blank">Variable rate technology (VRT)</a>, for instance, applies water, fertilizers, and pesticides at the right moment and location, maximizing efficiency and minimizing waste. Together, these Edge AI-driven practices represent a leap forward in sustainable agriculture, combining resource conservation with enhanced crop yields.Edge AI also plays a pivotal role in promoting sustainable agriculture by enabling practices that conserve resources and reduce chemical usage. By providing detailed insights into crop and soil health, it helps in implementing conservation tillage, cover cropping, and integrated pest management strategies more effectively. This not only supports the health of the ecosystem but also ensures long-term agricultural productivity and food security.Edge AI's applications extend beyond immediate farm-level benefits, laying a foundation for broader impacts on climate adaptation, economic sustainability, and informed policy-making. It is instrumental in adapting agricultural practices to changing climatic conditions and managing risks effectively. It also plays a crucial role in biodiversity conservation, monitoring ecosystem health, and promoting practices that sustain biodiversity within agricultural landscapes. The insights garnered from Edge AI applications can empower farmers with improved economic sustainability and influence agricultural policy-making, ensuring practices that are both productive and sustainable.<h3 dir="ltr">Streamlining Livestock Management with Edge AI</h3>Edge AI introduces a new era in livestock management, focusing on enhancing animal welfare while optimizing productivity. By employing real-time monitoring technologies, Edge AI systems track the health, behavior, and nutritional status of livestock, enabling early detection and treatment of illnesses, stress, or dietary deficiencies. This proactive approach not only improves the welfare of animals but also contributes to more efficient farming operations.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710791879865-22.jpg" style="width: 100%px;" class="fr-fic fr-dib">Edge AI and IoT can come together and enhance herd management, enabling real-time tracking of health and behavior. (Credit: Nvidia)From facial recognition for individual animal identification to automated systems for tailored nutrition and health management, Edge AI is transforming livestock care with precision and personalization. These technologies enable monitoring and management on a per-animal basis, improving the efficiency of breeding programs, and enhancing meat and milk quality. By leveraging Edge AI, farmers can make informed decisions that boost productivity while adhering to high welfare standards, showcasing a future where technology and traditional farming converge for superior outcomes.<h3 dir="ltr">Food Quality Assurance</h3>Edge AI plays a critical role in minimizing food waste and mitigating contamination risks throughout the supply chain. By employing advanced imaging and sensor technologies, Edge AI systems can detect early signs of spoilage or contamination in food products, allowing for immediate corrective actions.&nbsp;This capability is instrumental in ensuring that food storage conditions are optimized, significantly extending shelf life and reducing waste. Additionally, Edge AI aids in the identification of potential contaminants before products reach consumers, enhancing food safety and quality.The application of Edge AI in food production can go beyond waste reduction, significantly contributing to higher food safety standards. Through real-time monitoring and analysis, Edge AI can enable the detection of pathogens, toxins, and other harmful substances at various stages of the food supply chain.&nbsp;This proactive approach to food safety can not only help in preventing health risks but also boost consumer confidence in food products. Edge AI-driven systems can facilitate compliance with stringent food safety regulations, ensuring that products meet all necessary standards before distribution.Edge AI also streamlines supply chain operations, from demand forecasting to inventory management, ensuring the efficient delivery of fresh products. This operational efficiency is instrumental in reducing food waste and operational expenses. Furthermore, Edge AI fosters a transparent food system, where consumers gain insights into the food production journey, bolstering confidence and engagement.Edge AI is at the forefront of today’s agricultural revolution, driving advancements in crop monitoring, livestock management, and food quality assurance. Its ability to process and analyze data in real time is transforming traditional farming practices, making agriculture more efficient, sustainable, and productive. As we look to the future, the continuous evolution of Edge AI holds the promise of further innovations, ensuring food security and environmental conservation for generations to come.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712059065584-Mitsubishi+Chemical+Group++Growth+Garage++Open+Innovation+Program+%286%29.png" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="505" src="https://c8056756.sibforms.com/serve/MUIFAIvLRE9FbYyICZVAlYl6oaSYA1y_4gbHWS82SbbrrHstSB6VMs2aj57Tvg0e16L_UZVprsukgYH2yB927lpe-0uPg3ZEAhJiDPRQb2_oMEFQLYiEcG6zXHV6DwzxZPaJo25g3EzCRsYZ5z3ey0dkbIZE_M-AQ1kRN3i2_dq-vjEv7FQC29p_-Vu8tD5vbyaerZZF9IYqTQ8D" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>

Agriculture and Food Production 

<h3 dir="ltr" id="isPasted">dawdawd adwa An Edge-AI-Powered Automotive Sector</h3>In recent years, Edge AI has proven its value as a game-changer in the automotive industry by enabling real-time performance and various optimizations across different use cases of the transport and mobility ecosystem. Such use cases are found not only within popular AI systems like autonomous vehicles but also in use cases that foster more efficient traffic management.<blockquote>“Innovating with Edge AI in automotive isn't just about driving smarter cars; it's about redefining the road ahead, enhancing safety, and empowering vehicles to make split-second decisions, making every journey safer and more efficient." - – Samir Jaber, Editor-in-Chief</blockquote><h3 dir="ltr" id="isPasted">Autonomous Vehicles: Enhancing Safety and Efficiency on the Road</h3>Autonomous vehicles are at the forefront of disruptive innovation in the transportation sector. Edge AI is vital in enabling these vehicles to navigate and make critical decisions in real time based on the fast processing of a large volume of sensor data. Edge AI technologies are deployed in all five levels of autonomous vehicles, ranging from Level 0 (no automation) to Level 5 (full automation). Specifically, Edge AI systems improve the real-time functionalities of vehicles of lower automation levels while boosting the autonomy of vehicles that fall into higher levels of automation.&nbsp;For instance, Edge AI enhances the autonomy of partial-automation vehicles (i.e., Level 3) by boosting their advanced real-time perception, decision-making, and control functionalities. These functionalities help autonomous vehicles drive more safely and improve their environmental performance. Nowadays, relevant Edge AI functionalities can be implemented and deployed on different types of embedded devices of modern vehicles, such as On-Board Units (OBUs), enabling fast and real-time inference.&nbsp;Furthermore, Edge AI's integration with other technological trends, such as Autonomous, Connected, Electric, and Shared (ACES) mobility, unlocks synergistic benefits for the transport ecosystem. Based on Edge AI, ACES technologies can enable collaborative intelligence functionalities close to the field. Such functionalities include, for example, route optimization, congestion reduction, and enhanced energy efficiency.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710792265113-23.jpg" style="width: 100%px;" class="fr-fic fr-dib">The Holon Mover, an autonomous mini-bus (Credit: Benteler)<h3 dir="ltr" id="isPasted">Real-Time Traffic Management and Smart Parking</h3>Real-time optimizations are at the heart of efficient traffic management systems, enabling dynamic adaptations to rapidly changing conditions. Here, Edge AI empowers traffic management systems to process data from various sources (e.g., sensors, cameras, transport infrastructure) in real time. Analyzing such data at the network's edge enables the development of intelligent traffic management solutions that optimize signal timings, prioritize emergency vehicles, and dynamically adjust traffic flows. This way, Edge AI solutions help reduce congestion and improve transport efficiency.The real-time data processing functionalities of Edge AI are essential in use cases that involve busy intersections where split-second decision-making is required. For such cases, Edge AI enables the development of intersection control systems that identify and prioritize vehicles while being able to detect anomalies or potential hazards. Such functionalities are key to ensuring smooth and safe traffic flows.Furthermore, finding parking spaces in urban environments is one of the most pressing challenges, especially in highly populated megacities. With Edge AI, smart parking systems can provide real-time feedback about parking positions based on on-device AI. These systems leverage edge devices, such as parking sensors and cameras, to monitor parking occupancy and guide drivers to available spaces efficiently and in a timely fashion. Hence, Edge AI's processing power on edge devices enables smart parking systems characterized by low latency and enhanced real-time decision-making capabilities, improving the overall parking experience for drivers.<h3 dir="ltr" id="isPasted">Electric Vehicles: Enhancing Battery Management and Charging Infrastructure Optimization</h3>During the last few years, the world has increasingly embraced electric vehicles (EVs). EVs require battery management optimizations and the availability of EV charging infrastructures. To this end, Edge AI can facilitate real-time monitoring and analysis of EV batteries in ways that enhance their performance, lifespan, and overall reliability.&nbsp;Moreover, thanks to Edge AI algorithms, EVs can dynamically adjust charging patterns based on factors like battery condition, power availability, and user preferences. At the same time, Edge AI's integration with EV charging stations enables intelligent load management, balances energy demand, and optimizes charging schedules. Also, the proper distribution of the computational load at the edge can improve the grid's stability and efficiency by reducing strain during peak demand.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710792305637-24.jpeg" style="width: 100%px;" class="fr-fic fr-dib">The Canada Infrastructure Bank (CIB) and Quebec-based charging network operator FLO have announced a plan to install over 2,000 public DC fast charger ports across the country by 2027 (Credit: FLO).<h3 dir="ltr" id="isPasted">Fleet Management and Traffic Sign Recognition</h3>Edge AI empowers fleet management systems to collect and process a wealth of vehicle data, including location, fuel consumption, driver behavior, and vehicle health. The processing of this data at the edge provides fleet managers with real-time insights into fleet performance for implementing routing optimizations, reducing fuel consumption, and enhancing the operational efficiency of their fleet. Thus, Edge-AI-driven fleet management solutions can effectively help with cost reduction, improved logistics, and enhanced customer experience.Similarly, driver safety and compliance rely primarily on traffic sign recognition. Edge AI systems enable vehicles to detect and interpret traffic signs in real time. As such, they can provide drivers with real-time information about speed limits, traffic rules, and potential hazards. In this direction, advanced image processing algorithms can be deployed at the edge based on Edge AI approaches like TinyML for on-device image analysis. Therefore, Edge AI systems can enhance driver awareness, reduce the risk of accidents, and foster compliance with traffic regulations.The above-listed use cases indicate how Edge AI transforms the transport sector to enable real-time functionalities across various use cases. With its ability to process vast amounts of data at the edge, Edge AI empowers transport stakeholders to enhance safety, optimize operations, and improve the overall efficiency of modern transportation systems. Moreover, it helps reduce the attack surface of transport applications and limit the amount of potentially sensitive data (e.g., driving patterns information) from being shared within a cloud infrastructure. Edge AI can also boost privacy and data protection for consumer-facing applications in the transport sector. In the future, Edge AI systems will continue to play a significant role in shaping the evolution of the automotive and transportation industry.

Automotive and Transportation

<h3 dir="ltr" id="isPasted">Where Gen AI Meets Edge Computing</h3>The convergence of generative AI and edge computing is revolutionizing how AI interacts with its environment. Enterprises are increasingly deploying computing resources at the edge to capitalize on local data collection, filtering, aggregation, analysis, and generation. The trend of deploying generative AI at the edge is driven primarily by two key advancements: the development of relatively small, specialized models tailored for edge deployment and the availability of hardware acceleration for inferencing processes.&nbsp;These advancements enable the deployment of generative AI in edge environments, leading to applications such as voice-assisted shopper suggestions in retail, translation and emotion analysis of customer feedback, and autonomous decision-making in industrial environments and warehouses. In this chapter, we’ll take a deeper look at large language models (LLMs), what hinders their deployment at the edge, and how leading companies are finding ways to overcome these challenges.<blockquote>“Edge AI is changing dramatically; the new generation of hardware is capable of 500 times better performance than before, which has a massive impact on the world. It's difficult to fathom the full potential, but we do know that our ability to create accurate, high-performance Edge AI models is hugely expanded.” &nbsp;– Alexander Samuelsson, CTO of Imagimob</blockquote><h3 dir="ltr" id="isPasted">LLMs at the Edge: The Challenges</h3>The emergence of ChatGPT in November 2022 has led to a surge of interest in LLMs and their applications in various sectors. LLMs have revolutionized natural language processing (NLP) and natural language understanding (NLU) by enabling general-purpose conversational interactions while achieving remarkable performance on various language tasks. These models, such as GPT-3, Bard, LLaMa2, GPT-4, and Gemini, leverage neural networks with billions of parameters trained on vast amounts of data.&nbsp;This enables LLMs to generate coherent and contextually relevant text, which drives the development of many innovative applications. These applications are usually deployed within cloud-based infrastructures, as the growing size of LLMs poses challenges that inhibit their deployment on edge devices. The most prominent challenge to deploying LLMs at the edge is their sheer model size. LLMs with billions of parameters require substantial computing resources, which exceed the computational capabilities of most edge devices like smartphones or IoT devices. For instance, most IoT devices have limited memory and processing power, which makes it difficult to store and execute large models efficiently. Hence, deploying massive models on edge devices requires significant optimization efforts and hardware upgrades.Another major obstacle is the computational power required to utilize LLMs in real-time interactions. Edge devices often lack the computational capacity to process complex language models quickly. Therefore, applications requiring real-time interactions (e.g., chatbots, voice assistants) may not be feasible on edge devices with limited processing capabilities. The deployment of LLMs on such devices asks for sophisticated strategies to reduce latency and enhance performance.Despite these challenges, the concept of edge LLMs is very promising, as it comes with significant advantages like reduced latency and enhanced user experience. For instance, voice assistants or predictive text features can run directly on the device, eliminating the need for a constant internet connection and ensuring fast response times. Moreover, Edge LLMs can greatly enhance privacy and data protection for the growing base of LLM users. When processing prompts locally on the device, there is no need to transmit sensitive user information (e.g., information about the users’ tasks) to the cloud. This reduces the potential risks associated with data breaches or unauthorized access to user data.&nbsp;<h3 dir="ltr">Enabling LLMs and Edge Computing Convergence</h3>Nowadays, different solutions are under research and development to address the challenges of LLM deployment at the edge. These include model compression techniques, specialized hardware accelerators, and on-device training methods. They aim to minimize model size, reduce computational requirements, and achieve efficient execution on edge devices.One of the most prominent approaches to deploying LLMs on edge devices is reducing their model size. This can be achieved through techniques such as pruning, quantization, and distillation to produce smaller models while maintaining high performance. Pruning involves removing redundant parameters from the model, which results in a more compact representation. Quantization reduces the precision of numerical values used for computation, thus requiring less memory and processing power. Distillation compresses large models into smaller ones by using the output of one model as the target of another, leading to significant reductions in model size.Another approach involves the design and development of domain-specific models. Domain-specific applications enable the deployment of smaller and highly optimized models customized for particular use cases yet offer better efficiency and accuracy. Furthermore, domain-specific models enable edge devices to benefit from LLMs’ capabilities without relying on cloud infrastructures, which leads to increased privacy and faster response times.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710792414754-26.jpg" style="width: 100%px;" class="fr-fic fr-dib">BrainChip’s AKD1000 AIoT chip brings AI and edge together, leveraging its Akida™ advanced neural networking processor (Credit: BrainChip)<h3 dir="ltr" id="isPasted">Examples of Real-Life Edge LLM Systems</h3>One example of an LLM deployed on edge devices is an MIT-developed model called <a href="https://hanlab.mit.edu/blog/tinychat" target="_blank">TinyChat</a>. TinyChat is a neural conversational model that uses a small, domain-specific model to achieve real-time interactions on edge devices. The model is trained on data from a specific domain, such as customer support or technical assistance, which makes it more efficient and accurate in handling domain-specific queries.NVIDIA has also developed an embedded AI platform called <a href="https://www.nvidia.com/en-us/edge-computing/products/igx/" target="_blank">NVIDIA IGX Orin Developer Kit</a>, specifically designed to deploy LLMs on the edge. It offers high-performance computing power and memory capacity, making it ideal for running large models locally on devices like cars or robots. The kit uses NVIDIA's <a href="https://developer.nvidia.com/tensorrt" target="_blank">TensorRT</a> high-performance deep-learning inference framework. The latter optimizes models for deployment on edge devices towards faster inference times and lower power consumption.Qualcomm is also working on optimizing <a href="https://www.qualcomm.com/news/onq/2023/11/accelerating-generative-ai-at-the-edge" target="_blank">generative AI for edge devices</a>. Their efforts enable large models to perform real-time inferences on low-powered devices, which reduces applications’ reliance on cloud infrastructures. Qualcomm uses techniques such as on-device model compression and hardware acceleration to overcome the challenges of deploying LLMs on edge devices.<table style="width: 100%;"><tbody><tr><td style="width: 100%;">CASE STUDY: AXELERA<h2>Generative AI Meets Axelera's In-Memory Computing at the Edge</h2>At its core, Generative AI’s ability to craft new data boosts new applications. Large Language Models (LLMs) like GPT-4 drive conversations, and tools like Dall-E convert natural language input into images. A fast-evolving extension of this development is multi-modal Generative AI, where different input sources of text, audio, and video are analyzed and acted upon to generate new data.&nbsp; For businesses across the board, AI technology marks a transformative era, enabling dynamic optimization of operations for enhanced efficiency and environmental sustainability. For example, in surveillance and security, along with other sectors that rely heavily on vast volumes of text, audio, and video data, multi-modal Generative AI is set to introduce formidable capabilities for real-time anomaly detection and proactive intervention to prevent incidents from escalating. Furthermore, surveillance footage becomes queryable through simple prompts, allowing for comparing different feeds, generating situational descriptions, and providing simulated outcomes. In parallel, the manufacturing sector leverages machine vision and automation, enhanced by digital twin simulations, while the retail industry is witnessing the emergence of automated checkouts. Moreover, AI’s role extends to facilitating personalized shopping experiences and advancing smarter inventory management practices.&nbsp; How does this impact AI processing at the edge? Today, most of the AI processing is done in centralized data centers. However, compared to typical business computing, AI processing is considerably more compute- and data-demanding and typically resorts to using powerful but power-hungry GPUs (Graphics Processing Units). This creates sustainability challenges, heavy demands on network bandwidth, real-time delays, and privacy concerns.&nbsp; Enter Generative AI processing at the edge. Localized AI processing, closer to where the data is generated, can enhance privacy by not sending sensitive data to the cloud. Edge AI can also efficiently meet scalability challenges due to its more distributed model. However, implementing Generative AI at the edge presents new challenges, particularly in resource- and cost-constrained environments. As AI demands substantial processing power, a new breed of scalable, purpose-built AI acceleration solutions is emerging. Such novel technologies, like Axelera’s Digital In-Memory-Computing, can deliver remarkably high throughput while supporting programming flexibility, yet process AI models at a fraction of the cost and power consumption compared to traditional GPUs. The evolution of Generative AI at the edge heralds a significant transformation, necessitating a careful equilibrium between the opportunities it presents and the responsibilities it entails. It’s imperative to equip the workforce for this transition, ensure AI development is guided by ethical principles, and guarantee that the fruits of this technological evolution are accessible to all.</td></tr></tbody></table><h3 dir="ltr" id="isPasted">LLM at the Edge: Transforming Multiple Industries at Once</h3>Edge LLM deployments are likely to have a significant impact on various markets and application areas, bringing advanced conversational AI capabilities to the edge in real time. Here are some specific applications where Edge LLMs are expected to make a significant impact:<ul><li dir="ltr">Chatbots:&nbsp;Edge LLMs can improve the capabilities of state-of-the-art chatbots by enabling them to generate more intelligent and contextually relevant responses directly on edge devices. This reduces latency and the need for an internet connection, leading to faster and more efficient user interactions.</li><li dir="ltr">Virtual Assistants for Front Office Applications:&nbsp;Edge LLMs can enhance virtual assistants and front office applications by allowing them to process user queries locally. This is particularly useful in scenarios requiring real-time conversation, such as with voice assistants.</li><li dir="ltr">Code Generation and Code Completion:&nbsp;Edge LLMs can greatly benefit developers by providing code suggestions, code completion, and automatic code generation directly in their development environment. This can streamline the software development process, leading to increased productivity and efficiency in tasks such as Continuous Integration and Continuous Development (CI/CD) and other DevOps support tasks.</li><li dir="ltr">Text Generation by IoT devices:&nbsp;When deployed in cloudless environments like rural areas, IoT devices can leverage Edge LLMs to generate text in real time. For example, a smart agriculture device can analyze sensor data locally and provide recommendations on crop management, irrigation, or pest control without relying on an internet connection.</li><li dir="ltr">Training with Real-time Feedback: Edge LLMs can enable on-device training with real-time feedback. This is particularly beneficial in applications such as Augmented Reality (AR) and Human-Robot Collaboration (HRC), where instant feedback and adaptation are crucial for providing a pleasant and satisfactory user experience. For example, an AR application can use an Edge LLM to generate realistic virtual objects based on real-time information captured by the device's camera.</li></ul><table style="width: 100%;"><tbody><tr><td style="width: 100%;">CASE STUDY: BRAINCHIP<h2>Scaling Generative or Multi-modal AI</h2>The Generative AI phenomenon and the related Multi-modal AI have opened a new way of services and business along with an enormous challenge of exponentially increasing costs for training and inference. Activities like ASR (Automatic Speech Recognition) through raw audio, Text-to-Speech (and vice versa), and, with the advent of Augmented Reality, more contextual scene generation are among many possibilities driving demand for compute.&nbsp; The challenge is that the level of sensor data, LLMs, and Large Vision Models (LVMs) is too heavy to manage on the Edge device. Therefore, it drives a much larger load at the cloud, not to mention the challenges with real-time response, security, and privacy. With the slowing down of Moore’s Law and especially Dennard’s Scaling, power and energy benefits that were taken for granted don’t provide the expected gain. Therefore, there is a great deal of architectural innovation needed to empower Edge devices.&nbsp; BrainChip’s DNA is that of brain-inspired, neuromorphic computing, but taking it in a digital and event-based manner rather than traditional analog. In the second generation, BrainChip introduced the Temporal Event-based Neural Networks (TENNs) that are very adept at multi-dimensional streaming data. Initially, TENNs have shown their benefit for a wide variety of streaming data solutions – consuming raw audio signals or health care data without the need for filtering to infer audio or vital signs. Similarly, they show benefits in achieving video object detection in sub-watt power envelopes.&nbsp; Combining the new algorithms with some innovative hardware choices – especially those that make 3D convolutions very efficient – brings a big step in efficiency—demonstrating more than 100-500x improvement in energy efficiency without compromising accuracy. In terms of disruptive potential, TENNs could revolutionize LLMs and LVMs at the Edge. Tested on prior generation transformer-based models, a TENNs-equivalent has shown that for equivalent perplexity scores (an indicator of correctness), while reducing model size and MACs/token by 3-4 orders of magnitude. More importantly, the training of these models is similar to that of CNN training and yet takes less than 1/10th the time compared to the transformer equivalent.&nbsp; The result is that AkidaTM with TENNs can provide radical alternatives for Edge Devices that can now handle much more complex models in a small footprint solution for vision, surveillance, hearables, automotive, healthcare, and more. Check out our <a href="https://brainchip.com/wp-content/uploads/2023/06/TENNs_Whitepaper_Final.pdf" target="_blank" rel="noopener noreferrer" data-lf-fd-inspected-kn9eq4roawkarlvp="true">white paper</a> from BrainChip.</td></tr></tbody></table>Overall, the combination of generative AI and edge computing in hybrid edge-cloud deployments offers a wide range of benefits in terms of reduced latency, improved scalability, enhanced privacy, and cost efficiency. Edge AI vendors have already developed and demonstrated Edge LLM solutions, including their benefits in real-time applications like chatbots, virtual assistance and human-robot collaboration. As more Edge LLM applications emerge across various industries, the integration and use of edge and cloud resources with LLMs will play a crucial role in unlocking the full potential of generative AI.

Generative AI at the Edge

Edge AI Challenges and Real-World Mitigations

The Future of Edge AI

In the specialized field of sensor technology, ScioSense has carved out a significant niche under the guidance of its CEO, Stefan Raible, a seasoned expert with a background in physics and a passion for environmental and chemical sensors. Starting with a small startup launched with colleagues from the University of Tübingen, he has navigated the company's transformation into a small but mighty player in the sensor industry. This interview explores his insights into the evolution of sensor technology, particularly in the automotive sector, and his strategic vision for ScioSense’s future in addressing complex technological challenges.Wevolver: Can you introduce yourself and share your journey to becoming the CEO of ScioSense and how your background in environmental and chemical sensors influences your leadership approach?&nbsp;Stefan Raible:&nbsp;With an academic background in physics, I co-founded a company focused on environmental and chemical sensors in 2001 with colleagues from the University of Tübingen. The company has since evolved into what is now known as ScioSense. I have consistently engaged in the field of sensor technology due to its potential impact on daily life. Before becoming CEO of ScioSense, I held positions within the sensor industry at AppliedSensor as CTO and CEO, and at as Osram Business Line Manager and Engineering Fellow, where I focused on R&amp;D, product development, and strategic leadership.Wevolver: Reflecting on the evolution of automotive sensors in the past decade, could you discuss the cutting-edge innovations you anticipate will drive the sector forward in the next ten years?&nbsp;&nbsp;Stefan Raible:&nbsp;The past decade has seen significant advancements in sensor capabilities, particularly in areas such as autonomous driving and e-mobility. Environmental sensors play an important role, for example, in optimizing HVAC system performance in vehicles, thereby prolonging range, or by supporting ADAS systems with flow sensors to ensure that all sensors remain clean and operational. Additionally, environmental sensors have become smaller, more capable, and cheaper. This enables monitoring of air quality, dew point, battery safety, and other relevant measures around vehicle operation.Looking ahead, multi-sensor fusion, AI-driven data processing, and enhanced connectivity are expected to redefine automotive sensors. The integration of environmental sensing will support this development in various ways.Wevolver: What sets ScioSense apart in its approach to sensor design and development, particularly when pioneering new technologies for the future?&nbsp;Stefan Raible: ScioSense offers scalable, customer-oriented solutions for environmental and flow sensing that aim to foster a more sustainable future. Our sensors are designed to enhance safety, prevent losses, and improve efficiency. Notable examples include ultrasonic flow sensors for detecting water loss, dew point sensors for optimizing HVAC performance, and air quality solutions for enabling demand-controlled ventilation to reduce energy consumption. These innovations are driven by our skilled international team, which collaborates seamlessly across Italy, Germany, the Netherlands, and China. We work together with customers to define the next product, so we try to have a roadmap in alignment with our key customers.<iframe width="640" height="360" src="https://www.youtube.com/embed/W2a2IL3jIGg?&amp;feature=youtu.be&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> Wevolver: How do ScioSense's sensors make vehicles smarter, safer, and more efficient, particularly in areas like climate control, air quality monitoring, and autonomous systems?&nbsp;Stefan Raible:&nbsp;Our sensors enhance vehicle intelligence by providing real-time, high-accuracy data for various applications. In climate control, they optimize energy usage by adapting HVAC systems based on occupancy and external conditions. Our sensors detect pollutants and adjust ventilation for air quality monitoring, improving passenger comfort, health, and energy consumption.&nbsp;In battery systems, our sensors can detect thermal runaway incidents early and give warnings. In autonomous systems, our flow, environmental and gas sensors provide crucial situational awareness, ensuring safer and more reliable vehicle operations.

We sit down with Sciosense CEO, Stefan Raible to discuss the future of sensors and how collaboration is key to engineering success.

Test chapter

testicPowered by <a href="https://www.froala.com/wysiwyg-editor?pb=1" title="Froala Editor">Froala Editor</a>

New chapter to be scheduled

dwdwdPowered by <a href="https://www.froala.com/wysiwyg-editor?pb=1" title="Froala Editor">Froala Editor</a>

uhuuu

brand new one

Exploring the Dynamic World of Edge AI Applications Across Industries


2024 State of Edge AI Report

Our new report, AI at the Edge: Technology Accelerated by Wi-Fi Services, dives deeply into the convergence of Wi-Fi services and Edge AI and explores how a technology model like WaaS can accelerate the adoption of AI at the edge.&nbsp;Below is an excerpt from Chapter 1 of the report. To read the full report, including in-depth case studies, download it now.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5MTI3Njg4OTg0LVJlbGF5IDIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</iframe>Typically, artificial intelligence has been powered by cloud computing, given AI’s need for computing power and data-intensive processing. However, with the advent of edge computing, the AI landscape has been shifting, allowing for the rise of Edge AI. Edge AI is a revolutionary approach to how we deploy AI in various application areas, as it enables local computing and processing of data. That means the computing power is brought closer to the source of data, facilitating on-device data processing. With Edge AI, the system can make decisions in real time and with full contextual awareness by enabling AI features and capabilities directly on devices. Such a process would benefit from significant advantages brought about by Edge AI, including quick response, high bandwidth efficiency, and cost-effectiveness, to name a few.Unlike Cloud AI, which relies on remote servers for data processing and analysis, Edge AI is applied locally among a spectrum of devices spanning from data centers to IoT endpoints. The nature and location of the data being processed determine whether Cloud AI or Edge AI is more suitable for a particular application. For processing-intensive applications where latency can be compromised for the sake of in-depth data analysis and powerful computation, Cloud AI would be the ideal option. However, many applications are time-sensitive and require real-time data processing and prompt responses. This is where Edge AI takes center stage, minimizing the need to send data back and forth to a cloud server. This can take place at any location denoted as the edge, such as retail stores, hospitals, traffic lights, autonomous vehicles, and educational facilities.The following table explains the benefits and applicability of Edge AI and Cloud AI and how they differ from one another. Keep in mind that Edge AI and Cloud AI are not interchangeable technologies but rather applicable in different applications and scenarios. In fact, Edge AI and Cloud AI can even be complementary for systems that include both time-dependent and processing-intensive applications, which can benefit from a hybrid approach to data processing that integrates both technologies, as we’ll discuss later in this report.Table 1: Applicability and Benefits of Edge AI and Cloud AI<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA4NTg0MTM3NjM2LXRhYmxlIHJlbGF5LnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib"><h3 dir="ltr" id="isPasted">The Evolution and Adoption of Edge AI</h3>A 2023 report by Gartner on innovations in and around AI has highlighted the AI hype cycle for this year, showcasing the various advancements in AI and where they are today in terms of the public’s expectations . Among these innovations, we can see Cloud AI slowly navigating the so-called “Slope of Enlightenment” stage, where it is gradually transitioning toward mainstream adoption. In fact, Gartner expects Cloud AI to become mainstream in two to five years. Meanwhile, one of the fastest-growing innovations we can pinpoint on the cycle graph is Edge AI, showing a fast adoption rate to reach mainstream productivity in less than two years. This validates the potential that Edge AI has demonstrated in the past 2-3 years and shows that both edge and cloud computing can support AI applications in a symbiotic manner in the very near future.<a href="https://www.gartner.com/en/articles/what-s-new-in-artificial-intelligence-from-the-2023-gartner-hype-cycle" target="_blank" rel="noopener noreferrer"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA4NTg0MjE0MDU5LTAzXzExLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 768px;" class="fr-fic fr-dib">The hype cycle for AI in 2023 showed the rapid adoption rate of Edge AI. Credit: Gartner.</a>The recent developments in Edge AI have opened up new opportunities for devices to operate with human-like intelligence, regardless of their location. The successful deployment of AI models at the edge can be attributed to innovations that have significantly contributed to today’s digital transformation. For instance, neural networks and AI infrastructure have reached a level of maturity enough to pave the way for generalized machine learning. Organizations are now able to train AI models and deploy them in production at the edge. Besides, the recent advanced Edge AI accelerator chipset development, such as Axelera Metis, with high performance and low power budget, has made it possible to integrate these AI capabilities at a low power budget and with very high inference speed. Relay2 is pioneering this embedded integration with Wi-Fi Access Point, the edge of the Wi-Fi network. &nbsp;This is further strengthened by the widespread adoption of IoT devices, which is arguably the key reason behind the blowup of big data. Businesses can now collect every type of data from various sources. This availability of data and corresponding devices has made it possible to deploy AI models successfully at the edge.Given their remarkable ability to process and comprehend a range of unstructured data, including visuals, audio, temperature, language, faces, and more, AI algorithms have emerged as effective solutions for problems that end users face in real-time. Yet, deploying these AI applications primarily in centralized data centers like the cloud may prove impractical, especially with concerns about latency, bandwidth, and privacy. This is where the benefits of edge applications can facilitate AI deployment with their localized nature and decentralization, enabling real-time insights, high availability, and enhanced privacy.&nbsp;Nonetheless, Edge AI is not free of limitations, either. It still faces challenges, especially in data management, integration, security, miniaturization, and scalability. To overcome these challenges, a harmonious and purposeful co-design of hardware and software is necessary, and coordination with other technologies can prove quite fruitful. In fact, one of the most promising solutions for Edge Intelligence comes in the form of the convergence of Edge AI with cloud-based Wi-Fi Services, as we’ll see in the following section. Achieving miniaturization, model accuracy, energy efficiency, and cost-effectiveness simultaneously is a complex challenge. However, with proper coordination, optimization, and communication, we can facilitate the emergence of the next set of AI advancements at the edge.<h3 dir="ltr" id="isPasted">Next-Level Intelligence: Reinforcing Edge AI with Wi-Fi Services</h3>Since its inception about two and a half decades ago, Wi-Fi has shown significant development and adoption, with connection speeds skyrocketing from a few megabits to multi-gigabits per second. With this increase in speed, we have also seen an exponential rise in Wi-Fi-enabled devices, with almost every mobile device and computer now including Wi-Fi by default. In order to support the growing usage, demands, and throughputs of Wi-Fi, the network architecture has played a crucial role. Initially, Wi-Fi deployments were based on standalone access point architectures but later shifted to centralized hardware-based controller architectures. However, along with Wi-Fi functionalities like network optimization and mobility came some challenges, including scalability, reliability, manageability, and bottlenecks in performance, especially with increasing Wi-Fi traffic and link speeds. To tackle these challenges, service providers like Relay2 have been harnessing the power of the cloud and enabling vendors to shift from on-premise hardware controllers to cloud-native wireless controllers. Such a cloud-native system can provide the same benefits of centralized management without the constraints of physical hardware or associated adaptations. This not only enables greater flexibility but also offers more efficient and cost-effective management and coordinated control. As a result, this space has allowed for the rise of Wi-Fi-as-a-Service (WaaS), a subscription-based service that integrates software, infrastructure, and managed services to enable centralized management and coordinated control across different access points and automation of network monitoring and troubleshooting.Bringing Wi-Fi services and Edge AI into the same picture, we can see how their benefits align, where Wi-Fi enhances Edge AI’s scalability and integration capabilities, and Edge AI furthers the user experience of Wi-Fi solutions with data-driven insights and real-time analysis. For instance, looking at Edge AI’s integration challenges, we can still see cases of incompatibility stemming from differences in hardware, software, and communication protocols, which result from the unique architecture, specifications, and interface of every edge device. Employing Wi-Fi at this level enables secure, wide-coverage wireless communications, which further promotes interoperability and minimizes compatibility issues. Similarly, scalability challenges in terms of how Edge AI systems are handling increasing amounts of data and devices may compromise efficiency. The adaptability and scalability brought about by Wi-Fi services can help overcome these hurdles by enlarging a business’s network infrastructure and enabling effortless infrastructure management to meet the growing demand and shifting requirements.In other words, tying wireless and edge computing together can empower AI applications and reshape the business landscape to drive the digital transformation we are experiencing today. This convergence of Wi-Fi and edge computing presents a promising frontier for innovation, efficiency, and growth. As companies continue to recognize the value of this convergence, we can anticipate a marked increase in its adoption. As a result, edge computing would bring about a new wave of intelligent AI applications by integrating more computing power, which would allow adaptive learning and real-time responsiveness. This would be a significant step towards a more resilient system as the demand for always-on services continues to grow across various industries and application areas, including education, smart buildings, intelligent traffic management, smart agriculture, and smart retail. As more companies embrace the convergence of Wi-Fi and edge computing, we can expect to see a new era of technological innovation and efficiency.&nbsp;Right at the center of this technological fusion is Relay2, a leading Wi-Fi service provider that is on a mission to challenge the status quo of today’s Wi-Fi access point market. Relay2 is at the vanguard of the Wi-Fi edge-computing revolution, powered by their innovative ServiceEdge Platform, a Service Points technology that provides unparalleled performance, scalability, security, and privacy across a wide range of edge applications. In the next chapter, we will dive deeper into Relay2’s Wi-Fi services and explore how Relay2 is poised to reshape the Wi-Fi industry and how its patented platform is enabling more AI applications and capabilities within the edge-computing framework.This was an excerpt of Chapter 1 of the AI at the Edge: Technology Accelerated by Wi-Fi Services report. To read the full report, including in-depth case studies, download it below. &nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5MTI3ODU2MTAyLVJlbGF5IDIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;&nbsp;</iframe><h3 dir="ltr"> </h3><h3 dir="ltr" id="isPasted">Read excerpts of the other report chapters:</h3>Chapter 2: <a href="https://www.wevolver.com/article/ai-at-the-edge-report-chapter-2-relay2-and-edge-ai" target="_blank" rel="noopener noreferrer">Relay2 and Edge AI</a>&nbsp;Chapter 3: <a href="https://www.wevolver.com/article/ai-at-the-edge-report-chapter-3-applications-case-studies" target="_blank" rel="noopener noreferrer">Applications &amp; Case Studies</a>Chapter 4: <a href="https://www.wevolver.com/article/ai-at-the-edge-report-chapter-4-future-of-edge-ai-with-wi-fi-technology" target="_blank" rel="noopener noreferrer">Future of Edge AI with Wi-Fi Technology</a>&nbsp;

edge processors

Latest Posts