<h2 dir="ltr">Introduction</h2><p dir="ltr">To successfully deploy Edge AI applications with real-time capabilities and low latency, it is crucial to have access to high bandwidth connectivity that operates at ultra-fast speeds. This is where Wi-Fi comes into play. Deploying Wi-Fi technologies enhances Edge AI’s real-time analysis and reinforces its ability to proactively respond to diverse scenarios with minimal latency and higher bandwidth efficiency. Wi-Fi deployment is further streamlined by an advanced Wi-Fi-as-a-service (WaaS) business model.&nbsp;</p><p dir="ltr">Our new report, <strong>AI at the Edge: Technology Accelerated by Wi-Fi Services</strong>, 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;</p><p dir="ltr">Download the full 40-page report below.&nbsp;</p><p dir="ltr">&lt;image&gt;</p><p dir="ltr">Form.</p><h3 dir="ltr" id="isPasted">Leading the Digital Transformation with Edge AI and Wi-Fi-as-a-Service</h3><p dir="ltr">The report explore's how Relay2 is serving the global market and learn about its ServiceEdge Platform and PartnerConnect Program. We will also take a good look at some notable use cases of these technologies, including education, healthcare, and retail, and finally peek into the future of Edge AI and Wi-Fi.&nbsp;</p><p dir="ltr">This report aims to provide a comprehensive, in-depth analysis of how marrying Edge AI with cloud-managed Wi-Fi services will drive the growth of smart and lightweight infrastructure markets and create a smarter connected world with unified wireless access and an application computing platform.</p><blockquote><p dir="ltr">“Relay2 is pioneering the convergence of Wi-Fi and Edge Computing, fortifying industries to provide smarter and better-connected services, enhanced security, safety, and privacy, along with operational efficiency and cost optimization.” - Eric Chen, Founder &amp; CEO at Relay2</p></blockquote><p dir="ltr">Don't miss out on this comprehensive guide and detailed case studies. Download your copy now.&nbsp;</p><p dir="ltr" id="isPasted">&lt;image&gt;</p><p dir="ltr">Form.</p><h3 dir="ltr">Read excerpts of the report chapters here:</h3><p dir="ltr" id="isPasted">Chapter 1: Wi-Fi Services Enabling Edge AI</p><p dir="ltr" id="isPasted">Chapter 2: Relay2 and Edge AI</p><p>Chapter 3: Applications &amp; Case Studies</p><p dir="ltr" id="isPasted">Chapter 4: Future of Edge AI with Wi-Fi Technology</p><p>Conclusion: Wi-Fi-Powered Edge AI - A Technology for the Future</p>

<h2 dir="ltr">Introduction</h2><p dir="ltr">To successfully deploy Edge AI applications with real-time capabilities and low latency, it is crucial to have access to high bandwidth connectivity that operates at ultra-fast speeds. This is where Wi-Fi comes into play. Deploying Wi-Fi technologies enhances Edge AI’s real-time analysis and reinforces its ability to proactively respond to diverse scenarios with minimal latency and higher bandwidth efficiency. Wi-Fi deployment is further streamlined by an advanced Wi-Fi-as-a-service (WaaS) business model.&nbsp;</p><p dir="ltr">Our new report, <strong>AI at the Edge: Technology Accelerated by Wi-Fi Services</strong>, 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;</p><p dir="ltr">Download the full 40-page report below.&nbsp;</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1708692972720-Relay+final+%281%29.png" style="width: 300px;" class="fr-fic fr-dib"></p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable fr-active"><iframe src="https://c8056756.sibforms.com/serve/MUIFAHQ1RzmRq2C4LLaGIyc0SIbhplZVhzapUbUp7RqKP5Lt2roTAWGSz62uJMz4fNFeLGuhZI97irHq9J_4pLu2XbIx9FlrN9A8Gsmo11v03ozf6Qw83e4mk2E2ZWFuzWXMvR6NTo85ClJnyc7jmTIUWK4mQh9GbC-v_4SSumcw_XE_GIVo3OrFmb2b8rsyUNlGSVHQ7quSTDwC" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block; margin-left: auto; margin-right: auto; max-width: 100%; width: 540px; height: 455px;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></p><h3 dir="ltr" id="isPasted">Leading the Digital Transformation with Edge AI and Wi-Fi-as-a-Service</h3><p dir="ltr">The report explores how Relay2 is serving the global market and learns about its ServiceEdge Platform and PartnerConnect Program. We will also take a good look at some notable use cases of these technologies, including education, healthcare, and retail, and finally peek into the future of Edge AI and Wi-Fi.&nbsp;</p><p dir="ltr">This report aims to provide a comprehensive, in-depth analysis of how marrying Edge AI with cloud-managed Wi-Fi services will drive the growth of smart and lightweight infrastructure markets and create a smarter connected world with unified wireless access and an application computing platform.</p><blockquote><p dir="ltr">“Relay2 is pioneering the convergence of Wi-Fi and Edge Computing, fortifying industries to provide smarter and better-connected services, enhanced security, safety, and privacy, along with operational efficiency and cost optimization.” - Eric Chen, Founder &amp; CEO at Relay2</p></blockquote><p dir="ltr">Don't miss out on this comprehensive report with detailed case studies. Download your copy now.&nbsp;</p><p dir="ltr" id="isPasted"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1708693065073-relay+final+3+%281%29.png" style="width: 300px;" class="fr-fic fr-dib"></p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe src="https://c8056756.sibforms.com/serve/MUIFAHQ1RzmRq2C4LLaGIyc0SIbhplZVhzapUbUp7RqKP5Lt2roTAWGSz62uJMz4fNFeLGuhZI97irHq9J_4pLu2XbIx9FlrN9A8Gsmo11v03ozf6Qw83e4mk2E2ZWFuzWXMvR6NTo85ClJnyc7jmTIUWK4mQh9GbC-v_4SSumcw_XE_GIVo3OrFmb2b8rsyUNlGSVHQ7quSTDwC" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block; margin-left: auto; margin-right: auto; max-width: 100%; width: 540px; height: 455px;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true"><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></p><h3 dir="ltr">Read excerpts of the report chapters:</h3><p dir="ltr" id="isPasted">Chapter 1: <a href="https://www.wevolver.com/article/ai-at-the-edge-report-chapter-1-wi-fi-analytics-services-enabling-edge-ai" target="_blank" rel="noopener noreferrer">Wi-Fi Services Enabling Edge AI</a></p><p dir="ltr" id="isPasted">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;</p><p>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></p><p dir="ltr" id="isPasted">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;</p>

Introduction

Leading the Digital Transformation with Edge AI and Wi-Fi Networking

AI at the Edge: Technology Accelerated by Wi-Fi Services

<p id="isPasted">In the latest episode of the BrainChip Podcast, Ian Bratt, Senior Director of Technology at Arm, delves into the evolving landscape of AI and edge computing. This discussion is not just a peek into the current state of affairs but a roadmap to the future of AI, especially in the context of edge computing.</p><h2>AI at the Edge: An Insatiable Demand</h2><p>The conversation with Ian Bratt highlights a key trend in AI: the shift towards edge computing. Bratt points out the insatiable demand for AI across all levels of technology, emphasizing that significant AI workloads are increasingly being managed at the edge due to their growing demand. This shift is not just a trend but a necessity, driven by the need for more efficient and localized data processing.</p><h3>The Evolution of AI Models</h3><p>A fascinating aspect of the discussion revolves around the evolution of AI models. Initially, breakthroughs in AI lead to the creation of large, complex models. However, these models undergo a process of optimization, becoming more compact and efficient for deployment, including at the edge. This cycle of development and optimization is a continuous process in AI, reflecting the dynamic nature of the field.</p><h3>Challenges in Deploying Edge AI</h3><p>Deploying AI at the edge comes with its own set of challenges, as Bratt explains. Key among these is the need for efficient data collection and the design of robust neural networks capable of handling diverse scenarios. This part of the discussion is particularly relevant for engineers looking to implement AI solutions in real-world settings.</p><h2>The Role of Software and Ecosystems</h2><p>Bratt emphasizes the crucial role of software and ecosystem development in the success of AI. While efficient hardware is fundamental, the software and applications built on top of it are what truly unlock the potential of AI. This segment of the podcast is a call to action for developers and engineers to focus on creating software that maximizes the capabilities of AI hardware.</p><h3>Standardization: The Key to Edge AI Growth</h3><p>The podcast touches on an important issue in the field of edge AI: the need for standardization. The current fragmented nature of edge AI poses challenges, and Bratt discusses the push for standardization as a means to help the ecosystem grow. This includes the development of tools and frameworks that simplify the deployment of AI models on edge devices.</p><h3>Looking Ahead: The Future of Edge AI</h3><p>Bratt shares his vision for the future of edge AI, foreseeing devices that can run for years on minimal power, possibly through energy harvesting, while still performing sophisticated computing tasks. This forward-looking perspective is particularly exciting for engineers and developers working in the field.</p><h2>Conclusion: AGI and a Positive Future</h2><p>In a thought-provoking conclusion, Bratt expresses his optimism about the future of AI, particularly the potential for achieving Artificial General Intelligence (AGI). He envisions a future where AI leads to positive outcomes, countering dystopian narratives.</p><p>This episode of the BrainChip Podcast with Ian Bratt is informative and a beacon for those navigating the ever-evolving world of AI and edge computing. For engineers and tech enthusiasts, it's a glimpse into the future, filled with challenges to overcome and exciting possibilities to explore.</p>

Arm's Ian Bratt discusses the evolving landscape of AI and edge computing, emphasizing the growing demand for AI at the edge, the continuous optimization of AI models, and the importance of software and ecosystem development. 

BrainChip Podcast: Insights from Arm's Ian Bratt

<h1 dir="ltr" id="isPasted">Introduction</h1><p dir="ltr">Artificial intelligence (AI) is experiencing a significant transition with the increasing adoption of edge computing, where data processing occurs closer to the data source. The aim is to enable real-time decision-making and reduce dependency on cloud services.&nbsp;</p><p dir="ltr">This transition is driven by advanced microprocessors, with the Texas Instruments AM68A being a notable example. It offers a combination of efficiency, power, and adaptability, making it well-suited for the requirements of Edge AI applications.&nbsp;</p><p dir="ltr">In this article, we uncover the capabilities and implications of the Texas Instruments AM68A within the Edge AI landscape. Our exploration will dive into the intricacies of its technological features and spotlight its integration with the&nbsp;<a href="https://edgeimpulse.com/" target="_blank">Edge Impulse</a> platform. We'll also feature some case studies that demonstrate the practical application of the AM68A in Edge AI projects focused on computer vision.<br><br><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block; margin-left: auto; margin-right: auto; max-width: 100%; width: 766px; height: 603px;" 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></span></p><h1 dir="ltr"><br></h1><h3 dir="ltr">Overview of the Texas Instruments AM68A</h3><p dir="ltr">The Texas Instruments AM68A is a microprocessor that delivers great performance and features dedicated AI hardware acceleration. This enhancement is crucial for optimizing AI tasks directly on the device, enhancing efficiency and processing speed.&nbsp;</p><p dir="ltr">Before exploring its diverse uses and influence in Edge AI, it's essential to understand its core specifications and features. This groundwork will provide a clear perspective on the microprocessor's capabilities and how it can be utilized in various Edge AI contexts.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 304px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA0ODg1MDY3ODI1LWltYWdlMi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 302px;" class="fr-fic fr-dib" alt="Texas-Instruments-TI-AM68A"><span class="fr-inner" spellcheck="false">Fig. 1: Texas Instruments AM68A development platform</span></span></span></p><p dir="ltr" id="isPasted">Here are some core specifications and features of the AM68A:</p><div dir="ltr" align="left"><table><tbody><tr><td><p dir="ltr"><strong>Feature</strong></p></td><td><p dir="ltr"><strong>Description</strong></p></td></tr><tr><td><p dir="ltr"><strong>Processor Core</strong></p></td><td><p dir="ltr">Dual 64-bit ARM Cortex-A72 microprocessor subsystems, up to 2 GHz, with a shared 1MB L2 cache.</p></td></tr><tr><td><p dir="ltr"><strong>Deep Learning Accelerators</strong></p></td><td><p dir="ltr">Deep Learning Accelerator capable of up to 8 TOPS, for efficient AI computations and real-time data processing.</p></td></tr><tr><td><p dir="ltr"><strong>Vision Processing</strong></p></td><td><p dir="ltr">Vision Processing Accelerators, including an Image Signal Processor and multiple vision assist accelerators for handling image and video data.</p></td></tr><tr><td><p dir="ltr"><strong>Additional Processing Units</strong></p></td><td><p dir="ltr">Dual-core ARM Cortex-R5F MCUs for general compute operations and device management.</p></td></tr><tr><td><p dir="ltr"><strong>Graphics Processing</strong></p></td><td><p dir="ltr">IMG BXS-4-64, a high-performance 3D GPU supporting OpenGL ES 3.1 and Vulkan 1.2 for graphic-intensive applications.</p></td></tr><tr><td><p dir="ltr"><strong>Connectivity Options</strong></p></td><td><p dir="ltr">Gigabit ethernet, USB 3.0, CSI 2.0, PCIe gen 3, and various memory interfaces.</p></td></tr><tr><td><p dir="ltr"><strong>Security Features</strong></p></td><td><p dir="ltr">Secure boot and crypto hardware accelerators for robust security.</p></td></tr></tbody></table></div><p dir="ltr"><em>Table 1: Specifications and features of Texas Instruments AM68A</em></p><h1 dir="ltr">The Role of Deep Learning Accelerators in Edge AI</h1><p dir="ltr">The Texas Instruments AM68A has deep learning accelerators, which play a crucial role in Edge AI and computer vision applications. These are specialized hardware components designed to efficiently execute the computations required for deep learning algorithms. Unlike general-purpose processors, these accelerators are optimized for specific tasks like matrix multiplications and convolutions, which are fundamental in neural network operations.</p><p dir="ltr">The significance of deep learning accelerators in edge computing lies in their ability to bring advanced AI functionalities to devices operating at the edge of networks. By handling complex computations locally, these accelerators reduce the need to transmit large amounts of data to the cloud for processing. This cuts down on latency, making real-time processing feasible, and addresses concerns like bandwidth limitations and data privacy.</p><h3 dir="ltr">Real-World Applications and Case Studies</h3><p dir="ltr">The Texas Instruments AM68A has been central to a variety of computer vision projects, demonstrating its versatility and capability. These case studies showcase the practical use of the AM68A in diverse Edge AI applications.</p><h3 dir="ltr">1. American Sign Language (ASL) Translation</h3><p dir="ltr">One of the remarkable projects developed using a Texas Instruments board is an American Sign Language (ASL) Translation system. This application utilizes the AM62A, the entry-level alternative to the AM68A, and is designed to translate ASL into text in real-time. By leveraging machine learning algorithms and image processing capabilities, this system enhances communication accessibility for the deaf and hard-of-hearing community.&nbsp;</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA0ODg1MDk3MjkxLWltYWdlNC5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="sign-language"><span class="fr-inner" spellcheck="false">Fig. 2: Training the model to recognize different signs</span></span></span></p><p dir="ltr" id="isPasted">The project's success on the AM62A highlights the potential advancements that could be achieved with the more powerful AM68A.&nbsp;</p><p dir="ltr"><em><strong>Read more about this project:&nbsp;</strong></em><a href="https://docs.edgeimpulse.com/experts/image-projects/asl-byom-ti-am62a" target="_blank"><strong><em>Import a Pretrained Model with "Bring Your Own Model" - Texas Instruments AM62A</em></strong></a></p><h3 dir="ltr">2. Motorcycle Helmet Detection</h3><p dir="ltr">Another innovative application is the Motorcycle Helmet Detection project. Also developed on the Texas Instruments AM62A, this system utilizes machine learning and computer vision to detect whether motorcyclists are wearing helmets. This application is crucial for enhancing road safety and can be deployed at traffic signals or highway checkpoints.&nbsp;</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA0ODg1MTIwMzY2LWltYWdlNi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="helmet-detection-edge-ai"><span class="fr-inner" spellcheck="false">Fig. 3: Testing the motorcycle helmet detection system prototype</span></span></span></p><p dir="ltr" id="isPasted">The implementation with the AM62A demonstrates the feasibility of such safety applications, which can be further optimized with the AM68A's enhanced capabilities.&nbsp;</p><p dir="ltr"><em><strong>Explore this project in detail:&nbsp;</strong></em><a href="https://docs.edgeimpulse.com/experts/image-projects/motorcycle-helmet-detection-smart-light-ti-am62a" target="_blank"><strong><em>Motorcycle Helmet Identification and Traffic Light Control - Texas Instruments AM62A</em></strong></a></p><h3 dir="ltr">3. Posture Detection</h3><p dir="ltr">The Texas Instruments TDA4VM board, another counterpart of the AM68A with fewer features, has been used in a posture detection project. This application focuses on identifying incorrect postures in real-time to prevent workplace injuries. Using advanced image processing and AI, the system provides immediate feedback for posture correction.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA0ODg1MTQxOTU2LWltYWdlNS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="posture-detection-edge-ai"><span class="fr-inner" spellcheck="false">Fig. 4: Texas Instruments AM62A is used to efficiently distinguish between correct and incorrect postures, promoting ergonomic practices and preventing injuries.</span></span></span></p><p dir="ltr" id="isPasted">The AM68A, with its superior processing power, could elevate such health and safety applications to new levels.&nbsp;</p><p dir="ltr"><em><strong>Learn more about this project:&nbsp;</strong></em><a href="https://docs.edgeimpulse.com/experts/image-projects/ti-tda4vm-posture-detection" target="_blank"><strong><em>Correct Posture Detection and Enforcement - Texas Instruments TDA4VM</em></strong></a></p><h3 dir="ltr">4. Smart Factory Prototype</h3><p dir="ltr">Finally, the Smart Factory Prototype project, developed on the Texas Instruments TDA4VM, illustrates the potential of these boards in industrial applications. This system employs AI to monitor manufacturing processes, enhancing efficiency and predicting maintenance needs. The project demonstrates how edge computing can revolutionize industrial operations, and with the advanced features of the AM68A, these applications could achieve even greater efficiency and accuracy.&nbsp;</p><p dir="ltr"><em><strong>Read more about the Smart Factory Prototype:&nbsp;</strong></em><a href="https://docs.edgeimpulse.com/experts/image-projects/smart-factory-prototype-ti-tda4vm" target="_blank"><strong><em>Smart Factory Prototype - Texas Instruments TDA4VM</em></strong></a></p><h3 dir="ltr">Transitioning to the AM68A</h3><p dir="ltr">Each of these projects, developed on the AM62A and TDA4VM boards, lays the groundwork for what could be achieved with the AM68A. With its enhanced processing power and deep learning capabilities, the AM68A is poised to elevate these applications, offering more efficient processing, quicker response times, and the ability to handle more complex AI models. This transition represents a significant step forward in the capabilities of edge computing devices and their applications across various industries.</p><h2 dir="ltr">Conclusion</h2><p dir="ltr">The AM68A, with its advanced deep learning accelerators and enhanced processing capabilities, stands as a significant player in the rapidly evolving landscape of edge computing. Its integration with the Edge Impulse platform has opened new avenues for AI model development, making advanced AI functionalities more accessible and effective.</p><p dir="ltr">Throughout the article, we've seen how the AM68A is being utilized in various sectors, from enhancing road safety with motorcycle helmet detection to improving workplace ergonomics through posture detection. These applications, initially developed on the AM62A and TDA4VM, offer a glimpse into the possibilities with the more advanced AM68A. The chip's potential in driving innovations in smart factories, healthcare monitoring, environmental analysis, and beyond, underscores its versatility and efficiency in handling diverse Edge AI applications.</p><h1 dir="ltr"><em>About the sponsor: Edge Impulse</em></h1><p dir="ltr"><em>Edge Impulse is the leading development platform for embedded machine learning, used by over 1,000 enterprises across 200,000 ML projects worldwide. We are on a mission to enable the ultimate development experience for machine learning on embedded devices for sensors, audio, and computer vision, at scale.&nbsp;</em></p><p dir="ltr"><em>From getting started in under five minutes to MLOps in production, we enable highly optimized ML deployable to a wide range of hardware from MCUs to CPUs, to custom AI accelerators. With Edge Impulse, developers, engineers, and domain experts solve real problems using machine learning in embedded solutions, speeding up development time from years to weeks. We specialize in industrial and professional applications including predictive maintenance, anomaly detection, human health, wearables, and more.</em></p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA0ODg1MTczMTQ0LWltYWdlMS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="edge-impulse"></p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" 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></span><br></p>

High-performance microprocessors with AI hardware acceleration are revolutionizing Edge AI, enabling advanced computer vision capabilities directly on devices for real-time data processing and analysis.

Building Robust Edge AI Computer Vision Applications with High-Performance Microprocessors

<p dir="ltr" id="isPasted">In the latest BrainChip podcast hear from Zach about Edge Impulses astounding metrics for developer recruitment, training volume, and adoption; using AI to fill the gap when data sets are small or non-existent; the challenges of bringing AI to manufacturing and real-world use cases.</p><p dir="ltr"><a href="https://www.youtube.com/watch?v=f2hD-kHssg0" target="_blank">Listen to the full episode here.</a> Some of the highlights from the conversation, worth tuning in for include:</p><h2 dir="ltr">Reducing time and effort with automated data labeling and processing</h2><p dir="ltr">One of the pivotal advancements in AI and Edge Computing is the automation of data labeling and processing. The use of big segmenter models for auto-labeling imagery and other data types has been a game-changer, significantly reducing the time and effort required in data preparation. This automation is not just a matter of convenience but a critical step in enhancing the efficiency and accuracy of AI models. For engineers, this means an accelerated process from concept to deployment, enabling a more agile response to industry needs.</p><p dir="ltr">The implications of automated data labeling extend beyond mere efficiency. It represents a shift in how engineers approach data - from a task-intensive process to a more streamlined, model-driven approach. This shift is crucial in industries where the volume of data is vast and the need for precision is paramount. The ability to quickly identify and utilize relevant data points without extensive manual intervention is a substantial leap forward in AI and Edge Computing.</p><h3 dir="ltr">The challenge of LLMs and the power of the developer ecosystem<br><br></h3><p dir="ltr">Despite the advancements, the deployment of AI models, especially Large Language Models (LLMs), in edge computing environments poses significant challenges. The reliability of these models in critical scenarios, such as industrial maintenance or medical diagnostics, is a primary concern. Engineers are tasked with ensuring that these models provide accurate and reliable outputs every time, a non-negotiable requirement in high-stakes environments.</p><p dir="ltr">The podcast highlights the growth in the developer ecosystem, particularly in platforms like Edge Impulse, which has seen its community double in size within a year. This growth is indicative of the increasing interest and adoption of AI and edge computing technologies across various sectors. Real-world deployments in healthcare, logistics, and manufacturing are testament to the practical applications of these technologies. From medical transcription to predictive maintenance in manufacturing, the scope of AI and Edge Computing is vast and continually expanding.<br><br></p><p dir="ltr">However, the journey from training to deployment is fraught with complexities. Engineers must navigate the challenges of diverse hardware environments and the unique demands of edge computing scenarios. This requires a deep understanding of both the technological and practical aspects of AI model deployment, ensuring that the solutions are not only technologically sound but also viable in real-world settings.<br><br></p><h3 dir="ltr">Beyond the hype: Making AI practical and reliable</h3><p>The industry anticipates a shift towards more practical and reliable AI solutions. The focus is expected to move away from pursuing exotic innovations to establishing clear guidelines and stable hardware for successful AI and edge computing applications. For engineers, this means a greater emphasis on developing solutions that are not only advanced but also robust and easily deployable in various environments.<br><br></p><p dir="ltr">Collaborations, like the one between Edge Impulse and Nvidia, are pivotal in this context. They bring foundational AI models to the edge, optimizing them for diverse hardware requirements. Such partnerships are crucial in bridging the gap between cutting-edge AI research and practical, deployable solutions.<br><br></p><p dir="ltr">As the technology evolves, the focus for engineers will increasingly be on creating solutions that are not just innovative but also reliable, efficient, and tailored to specific industry needs. The future of AI and Edge Computing is poised to be driven by practicality, reliability, and a deep understanding of the real-world contexts in which these technologies will operate.</p><p><a href="https://www.wevolver.com/profile/brainchip" target="_blank">Read more about BrainChip here.</a></p>

In the latest BrainChip podcast episode, host Nandan Nayampally sat down with Zach Shelby, CEO of Edge Impulse, to talk about AI at the Edge. 


BrainChip Podcast:  AI after the Hype Cycle

<h2>Introduction to Temporal Event-Based Neural Networks (TENNs)</h2><p dir="ltr">In the realm of Artificial Intelligence (AI), the evolution of neural network technologies has reached a pivotal juncture with the advent of Temporal Event-Based Neural Networks (TENNs). Developed by BrainChip, TENNs represent a groundbreaking approach to processing sequential spatiotemporal data, addressing the limitations of traditional neural network models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).</p><p dir="ltr">CNNs, while proficient in processing spatial information in images, falter in effectively utilizing temporal data. RNNs, on the other hand, capture sequential dependencies but face challenges like vanishing gradients and difficulty in parallelization. TENNs emerge as a solution, efficiently combining spatial and temporal convolutions, thus excelling in processing sequential data like video and time series data.</p><p dir="ltr">TENNs leverage the strengths of both CNNs and RNNs, integrating spatial and temporal characteristics of data. This integration enables TENNs to learn spatial and temporal correlations effectively, distinguishing them from traditional state-space models. Their flexibility allows for operation in either temporal convolution mode or recurrent mode, adapting to specific application requirements.</p><p dir="ltr">BrainChip's 2nd Generation Akida platform supports TENNs, offering state-of-the-art performance across various domains of sequential data. This includes applications in speech recognition, patient monitoring in medical equipment, and streaming video object detection and tracking. With their superior performance, low computational requirements, and versatility, TENNs are poised to revolutionize intelligent Edge solutions, providing highly accurate models for sequential data analysis with a fraction of the computational resources required by other network architectures.</p><p dir="ltr">For a deeper understanding of TENNs and their applications, BrainChip's white paper, "Temporal Event-based Neural Networks: A New Approach to Temporal Processing," offers comprehensive insights into this innovative technology.</p><p dir="ltr"><a href="https://wevlv.co/brainchip-tenns-whitepaper" target="_blank" rel="noopener noreferrer">Read the whitepaper here. </a></p><h3 dir="ltr">More about BrainChip: Making AI ubiquitous</h3><p>The integration of sophisticated IoT infrastructure with Artificial Intelligence technologies, particularly those modeled after human neural processes, results in a significant enhancement of operational efficiency. This amalgamation facilitates proactive problem-solving, augments interactions between humans and machines, and fortifies security measures.</p><p dir="ltr">A crucial aspect of proliferating AI ubiquitously lies in simplifying its deployment and scalability. Achieving this necessitates a reduction in costs, autonomy from cloud reliance, and the localization of intelligence in proximity to sensory inputs.</p><p dir="ltr">Brainchip's comprehensive and decentralized strategy alleviates the burden on cloud resources, thereby catalyzing a surge in capable and intelligent sensing devices at the Edge. This approach is instrumental in accelerating the global advancement of artificial intelligence, marking a significant milestone in the AIoT domain.</p><p dir="ltr">Brainchip’s holistic, distributed approach frees up the cloud and creates an explosion of capable, intelligent sensing devices on the Edge that accelerate global artificial intelligence.</p>

Learn more about a groundbreaking solution called Temporal Event-based Neural Networks (TENNs) developed by BrainChip, which efficiently combines spatial and temporal convolutions to process sequential data like never before.

TENNs: Unlocking the Power of Efficient Processing for Sequential Data

Danny Shapiro, the Vice President of Automotive at NVIDIA discusses the challenges, breakthroughs, and vision that are propelling autonomous vehicles into the next era.

2023 Autonomous Vehicle Report Interview: Insights from NVIDIA about Artifical Intelligence in AVs

<h2 dir="ltr" id="isPasted">Event Overview</h2><p dir="ltr">Hackster's Impact Summit is a free two-day virtual event (14th-15th November, 2023) dedicated to inspiring developers, connecting organizations, and accelerating technological innovation. This year's topic examines how to build resilient and sustainable cities.&nbsp;</p><h2 dir="ltr">Who Should Attend</h2><p dir="ltr">The Impact Summit is inviting technologists, engineers, community trailblazers and technology providers. The event offers an opportunity to examine the transformative role of machine learning, advanced data analytics, and IoT in enhancing the management and maintenance of urban infrastructure, public services, and critical facilities.</p><h2 dir="ltr">Session Highlights</h2><p dir="ltr"><strong>Day 1</strong> commences with Kate Kallot, CEO of Amini, examining the harmony of socioeconomic growth with environmental conservation. Followed by a workshop on predictive maintenance using Nordic Thingy:53, led by David Tischler from Edge Impulse. The day progresses with a demo on SCADA system modernization with Bryan Costanich, Wilderness Labs CEO, and concludes with a panel discussion on urban environment innovation and several workshops.</p><p dir="ltr"><strong>Day 2</strong> opens with a keynote from Tomas Diez, Executive Director of Fab City Foundation, discussing community-driven sustainable urban development. The day continues with a panel on technology resiliency, workshops on sustainable city AI and embedded programming, and concludes with a demo on frugal innovation for urban development by Kevin Schmidt from UNDP.</p><p dir="ltr"><a href="https://wevlv.co/impactsummit2023" target="_blank" rel="noopener noreferrer" class="button-main-action">Learn more now</a></p><h2 dir="ltr">Insights</h2><p dir="ltr">The summit features speakers from leading organizations and companies who are at the forefront of urban technology and development. They will share real-world applications and insights into the impact of technological innovation on city living.</p><h3 dir="ltr">Technical Deep Dive</h3><p dir="ltr">Attendees will benefit from a practical approach to smart city challenges, with in-depth technical workshops on IoT, AI at the edge, and predictive maintenance. These sessions will equip participants with knowledge applicable to real-world urban systems.</p><h3 dir="ltr">Interactive Engagement</h3><p dir="ltr">Live panel discussions provide a platform for interactive dialogue between technology experts, policymakers, and attendees, focusing on the translation of ideas into tangible urban solutions.</p><h3 dir="ltr">Learning Outcomes</h3><p dir="ltr">Participants will leave with actionable strategies for deploying technology in urban infrastructure, insights into sustainable development practices, and a comprehensive understanding of the predictive maintenance landscape.</p><h3 dir="ltr">Event Accessibility</h3><p dir="ltr">Sessions are scheduled in Central Time (CST).</p><p dir="ltr">Post-event access to session recordings will be provided to all registrants.</p><p dir="ltr"><a href="https://wevlv.co/impact-summit-zoom" target="_blank" rel="noopener noreferrer" class="button-main-action">Register now</a></p><p dir="ltr">Would you like to learn more about the Impact Summit? <a href="https://wevlv.co/impactsummit2023" target="_blank" rel="noopener noreferrer">Visit this page.</a></p>

Accelerating Urban Efficiency through Technology. Explore How AI & IoT Drive the Future of Sustainable Urban Living



Impact Summit 2023 Virtual Event

<p dir="ltr" id="isPasted">In engineering, quality control ensures the reliability, safety, and consistency of products and processes. As industries evolve, the demand for continual improvement becomes paramount, not just to stay competitive but also to guarantee the highest standards of safety and reliability. In this dynamic landscape, the introduction of Edge AI emerges as a transformative solution, poised to revolutionize quality control mechanisms.</p><p dir="ltr">Quality control, especially in engineering environments, is not just about maintaining standards; it's about elevating them. The competitive nature of today's industries necessitates a relentless pursuit of perfection. Traditional methods, while effective, often need more agility and real-time responsiveness than modern manufacturing and production environments require. This is where the need for continual improvement and innovation comes into play.</p><p dir="ltr">Edge AI, a technology that emphasizes processing data at its origin rather than relying on a centralized data center, presents a sophisticated advancement in quality control. By integrating AI capabilities directly into devices and systems on the manufacturing floor, Edge AI facilitates real-time analysis, immediate feedback, and rapid decision-making. Such capabilities are not just enhancements; they are transformational, enabling businesses to detect anomalies, predict issues, and ensure optimal quality at unprecedented speeds.</p><p dir="ltr">Drawing insights from various industry studies and advancements, it's evident that Edge AI's integration into quality control processes offers a promising avenue for industries to stay ahead, ensuring both competitiveness and unparalleled quality standards.</p><p dir="ltr">Let's delve deeper!</p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" 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></span><br></p><h2 dir="ltr">The Imperative of Quality Control in Engineering</h2><p dir="ltr">Quality control has traditionally been a reactive process, heavily reliant on post-production inspections and tests. Traditional methods, while foundational, have their limitations:</p><p dir="ltr">Traditional Quality Control Systems: Historically, quality assurance has been downstream-focused, emphasizing production and testing. This approach, often characterized by repetitive design-prototype-test cycles, can be likened to a trial-and-error method. Such methods might only sometimes yield optimal designs, leading to increased costs, extended cycle times, and potential compromises in customer quality.&nbsp;</p><p dir="ltr">The traditional system operates on the premise that as long as a product is within specification limits, it meets customer requirements. However, the business implications of a product being "barely within specification limits" and "right on target" are vastly different.</p><p dir="ltr">Financial and Operational Implications: Inadequate quality control can have severe financial repercussions. Products that don't meet quality standards can lead to recalls, warranty claims, and loss of customer trust. Operationally, inadequate quality control can result in increased wastage, rework, and delays in production schedules.</p><p dir="ltr">Evolving Standards and Expectations: With the rapid advancements in technology and globalization, industry standards are continually evolving. Customers today are more informed and have higher expectations. They demand products that are functional but also safe, durable, and innovative. This drives the need for more robust and proactive quality control processes.</p><p dir="ltr">The limitations of traditional quality control methods and the changing landscape of customer expectations underscore the need for innovative solutions. As industries strive for perfection, there's a pressing demand for methods that are reactive but also proactive, predictive, and efficient.</p><h2 dir="ltr">The Advent of Edge AI in Quality Control</h2><p dir="ltr">As traditional methods have inherent limitations, the industry is turning to innovative solutions such as Edge AI.</p><p dir="ltr">Edge AI refers to deploying artificial intelligence algorithms directly on the devices where data is generated rather than processing this data in a centralized cloud-based system. This proximity to the data source facilitates real-time data processing, ensuring swift responses and immediate insights.</p><h3 dir="ltr">Addressing Traditional Limitations</h3><p dir="ltr">Traditional quality control methods, while foundational, often lag in real-time responsiveness and agility. Edge AI, with its on-device processing capabilities, addresses these gaps. By analyzing data at the source, it eliminates the latency associated with transmitting data to a central server for processing. This not only speeds up the quality control process but also enhances its accuracy and efficiency.</p><h3 dir="ltr">Benefits of Edge AI in Quality Control</h3><p dir="ltr">Real-time Anomaly Detection: With Edge AI, anomalies in the manufacturing or production process can be detected in real-time, allowing immediate corrective actions.</p><p dir="ltr">Predictive Maintenance: Edge AI can predict equipment failures before they occur, ensuring uninterrupted operations and reducing downtime.</p><p dir="ltr">Reduced Operational Costs: By processing data locally, Edge AI reduces the need for data transmission and cloud storage, leading to potentially significant cost savings. Additionally, the efficiency gains from real-time insights and predictive maintenance further contribute to operational cost reductions.</p><p dir="ltr">Integrating Edge AI into quality control processes signifies a monumental shift from reactive to proactive quality assurance. As industries continue to evolve, technologies like Edge AI will play a pivotal role in ensuring that quality remains at the forefront of all operations.</p><h2 dir="ltr">Spotlight on Edge Impulse</h2><p dir="ltr">In the rapidly evolving domain of Edge AI, Edge Impulse stands out as a pioneering platform, offering a comprehensive suite of tools and capabilities tailored for deploying machine learning models on edge devices.</p><p dir="ltr">Edge Impulse is a cutting-edge platform designed to simplify creating and deploying AI models on edge devices. By providing a user-friendly interface, Edge Impulse allows engineers and developers to harness the power of AI without the complexities often associated with traditional machine learning workflows. One of the standout features of Edge Impulse is its ability to streamline the deployment of machine learning models directly on edge devices.&nbsp;</p><h3 dir="ltr">Key Features of Edge Impulse</h3><p dir="ltr">Data Collection: Edge Impulse Studio offers a data acquisition tool that allows users to upload datasets, facilitating the training and validation of machine learning models.</p><p dir="ltr">Model Training: The platform provides various tools and algorithms, including the YOLOv5 object detection model, which can be optimized for resource-constrained devices.</p><p dir="ltr">Deployment: After training, Edge Impulse facilitates the deployment of the model on edge devices, such as the <a href="https://www.renesas.com/us/en/products/microcontrollers-microprocessors/rz-mpus/rzv2l-evkit-smarc-som-evaluation-kit-rzv2l-mpu-ai-accelerator" target="_blank">Renesas RZ/V2L Evaluation Board</a>, ensuring seamless integration and real-time performance.</p><p dir="ltr">Edge Impulse's capabilities extend beyond just quality control in manufacturing. Its versatile platform can be adapted to various applications, making it a valuable tool for industries aiming to leverage the benefits of Edge AI.</p><h2 dir="ltr">Edge Impulse in Action: A Case Study</h2><p dir="ltr">Quality inspections are pivotal in ascertaining that products meet the required standards and specifications. A case study involving Edge Impulse offers a deep dive into how cutting-edge technology can elevate these inspections, ensuring optimal product quality.</p><p dir="ltr">The Challenge: Manufacturers often grapple with establishing a comprehensive inspection procedure encompassing all facets of a product's quality. Traditional methods, while effective, may only sometimes offer the agility and real-time responsiveness required in modern manufacturing environments. The challenge lies in seamlessly integrating the inspection process into the existing workflow without causing disruptions, all while maintaining consistent quality across batches.</p><p dir="ltr">The Edge Impulse Solution: Solomon Githu, an engineer, envisioned a <a href="https://www.hackster.io/sologithu/product-inspection-with-renesas-rz-v2l-and-edge-impulse-cf0700" target="_blank">solution</a> that would seamlessly integrate into existing processes and autonomously discern product quality. He conceptualized an inspection device employing computer vision and machine learning to inspect products as they traverse an assembly line swiftly.&nbsp;</p><p dir="ltr">The objective was clear: the device should autonomously determine product quality in milliseconds.</p><p dir="ltr">To bring this vision to life, Githu leveraged the <a href="https://edgeimpulse.com/product" target="_blank">Edge Impulse Studio</a>. The platform's web-based interface replaced the complexities of traditional machine learning with a user-friendly, point-and-click experience. Using an object detection algorithm, the system was trained to inspect products and discern their quality rapidly. The training involved a dataset comprising both high-quality products and those with defects.</p><p dir="ltr">To ensure real-time responsiveness, Githu employed the <a href="https://www.renesas.com/us/en/products/microcontrollers-microprocessors/rz-mpus/rzv2l-evkit-smarc-som-evaluation-kit-rzv2l-mpu-ai-accelerator" target="_blank">Renesas RZ/V2L Evaluation Board</a>, specifically designed for computer vision and machine learning applications. Paired with a <a href="https://coral.ai/products/camera/" target="_blank">Google Coral camera</a>, the system was equipped to inspect products and make real-time determinations visually.</p><p dir="ltr">Outcomes: The system was tested using a custom-built miniature conveyor belt, with printed images of products (in this case, submersible pump impellers) moving along it. The camera, positioned above, relayed real-time visuals to a custom web application, which then displayed the products and maintained a count of the good and defective items it detected. The results were compelling: the system showcased high accuracy, aligning with the impressive performance metrics observed during model training and testing.</p><p dir="ltr">Conclusion: This case study underscores the transformative potential of Edge Impulse in the realm of quality control. By harnessing the power of Edge AI, manufacturers can elevate their quality inspection processes, ensuring that products not only meet but exceed the desired quality standards.</p><h2 dir="ltr">The Road Ahead</h2><p dir="ltr">Integrating Edge AI into quality control processes has showcased transformative results in various engineering domains. As industries continue to evolve and the demand for real-time, efficient, and accurate quality control mechanisms grows, the potential for further integration of Edge AI becomes even more pronounced.</p><p dir="ltr"><strong>Potential Across Engineering Domains:&nbsp;</strong>While the manufacturing sector has been an early adopter of Edge AI for quality control, other engineering domains, such as civil engineering, electronics, and aerospace, could benefit immensely from this technology. The ability to process data in real-time at the source can lead to enhanced structural integrity checks, real-time monitoring of electronic components, and swift anomaly detection in aerospace systems.</p><p dir="ltr"><strong>Scalability and Adaptability of Edge Impulse:</strong> Platforms like Edge Impulse are designed with scalability and adaptability in mind. Whether adapting to different data types, integrating with various edge devices, or scaling to handle vast amounts of data, Edge Impulse ensures that diverse quality control requirements across industries are met precisely. The platform's capabilities, such as data collection, model training, and deployment, can be tailored to suit specific industry needs, making it a versatile tool for quality control.</p><p dir="ltr"><strong>Meeting Diverse Quality Control Requirements:</strong> As industries diversify and products become more complex, the quality control requirements also evolve. Edge AI platforms, with their adaptability, can cater to these diverse needs, ensuring that quality control is not just about meeting standards but setting new benchmarks.</p><h2 dir="ltr">Conclusion</h2><p dir="ltr">Integrating Edge AI into quality control processes signifies a significant stride in the engineering domain. Platforms like Edge Impulse have showcased the capability to enhance these processes, offering real-time data processing and decision-making at the source. Such advancements are not just about improving efficiency but also about ensuring consistent and high-quality outputs.</p><p dir="ltr">Edge Impulse, in particular, has demonstrated its prowess in facilitating the deployment of machine learning models on edge devices. It is a valuable tool for industries aiming to improve quality control mechanisms. Its capabilities, ranging from data collection to model training and deployment, are tailored to meet the specific needs of various industries, ensuring adaptability and scalability.</p><p dir="ltr">For engineering firms, the message is clear: the future of quality control lies in harnessing the power of Edge AI. By exploring and adopting solutions like Edge Impulse, firms can position themselves at the forefront of technological advancements, ensuring they meet the evolving standards of quality and efficiency in the industry.</p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" 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></span><br></p><h2 dir="ltr">References</h2><ol><li dir="ltr"><p dir="ltr"><a href="https://metrology.news/re-imagining-manufacturing-quality-with-ai-and-edge-computing/" target="_blank">Re-imagining Manufacturing Quality with AI and Edge Computing</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://edgeimpulse.com/blog/elevating-quality-standards-with-ai" target="_blank">Elevating Quality Standards with AI</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://www.griddynamics.com/events/quality-control-manufacturing-webinar-q3-2022" target="_blank">Quality 4.0: Reimagining AI and Quality Control Automation for Smart Manufacturing</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://developer.ibm.com/articles/enabling-distributed-ai-with-edge-computing/" target="_blank">Enabling Distributed AI for Quality Inspection in Manufacturing with Edge Computing</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://edgeimpulse.com/blog/elevating-quality-standards-with-ai" target="_blank">https://edgeimpulse.com/blog/elevating-quality-standards-with-ai</a></p></li></ol>

Revolutionizing Engineering Quality Control: The Transformative Power of Edge AI

Edge AI: A Paradigm Shift in Quality Control

<p dir="ltr" id="isPasted">For those eager to grasp the intricacies of AI and neuromorphic computing BrainChip's podcast series stands out as a strong source of knowledge. In their 28th episode titled "This Is Our Mission," BrainChip's Rob Telson orchestrates a riveting conversation with Tenstorrent's COO, Keith Witek, and BrainChip's CMO, Nandan Nayampally. This episode provides a treasure trove of insights, making it a must-listen for engineers working in the industry or warning to know more about the cutting edge of computing.</p><h2 dir="ltr">About the Guests</h2><p dir="ltr">Rob Telson, the Vice President of Ecosystem and Partnerships at BrainChip, is the dynamic force behind the podcast series. With a keen eye for curating meaningful discussions, Telson ensures that each episode is both enlightening and engaging. His role at BrainChip involves fostering partnerships and building a robust ecosystem, making him the perfect host to bridge the gap between technical intricacies and their real-world implications.&nbsp;</p><p dir="ltr">Keith Witek, the COO of Tenstorrent, boasts an impressive resume. With degrees spanning Business, Computing, Electrical Engineering, and Law, Witek has left his mark at tech giants like Google and Tesla. His expertise in strategic alliances, especially in consumer electronics, sets the stage for a profound discussion.</p><p dir="ltr">Nandan Nayampally, BrainChip's CMO, brings to the table a rich history in the semiconductor domain. With stints at AMD, Denali Software, and even Amazon's Alexa, Nayampally's insights into computing trends are invaluable.</p><h2 dir="ltr">Podcast Key Discussion Points</h2><p dir="ltr">AI's Staying Power: The realm of technology is no stranger to fleeting trends, but AI is not one of them. As discussed in the podcast, AI has firmly established itself as a transformative force, with its applications and implications reaching far and wide. The consensus between the experts is clear: AI's influence in shaping the future is undeniable, and its role in various sectors is only set to grow.</p><p dir="ltr"><strong>Real-world AI Impacts:</strong> The podcast delves into tangible ways AI is making a difference in our lives. From the significant reduction in car-related fatalities due to AI-driven safety features to the potential of personalized healthcare solutions tailored to individual needs, AI's footprint in real-world scenarios is expanding rapidly.</p><p dir="ltr"><strong>Challenges in AI Scalability:</strong> Despite its vast potential, AI faces its set of challenges. The podcast highlights issues like the massive influx of data that systems need to process and the impending slowdown of Moore’s Law. These challenges pose questions about the scalability and efficiency of current AI models.</p><p dir="ltr"><strong>Innovative Solutions:&nbsp;</strong>The conversation takes an optimistic turn when discussing the solutions. Industry leaders, including BrainChip and Tenstorrent, are at the forefront of addressing these challenges. Their innovative approaches and technologies promise to overcome existing barriers, paving the way for a more AI-integrated future.</p><p dir="ltr"><strong>Promising Tech Deployment Models:</strong> The podcast also touches upon the future of technology deployment. The concept of chiplets, for instance, offers a glimpse into the potential shifts in how we might approach hardware design and integration in the coming years, ensuring more efficient and scalable AI implementations.</p><h2 dir="ltr">Closing Thoughts</h2><p dir="ltr">This podcast episode is a great resource for anyone interested in the future of AI, regardless of their background or level of knowledge. It promises to offer insights into real-world applications, challenges, and solutions that are shaping the evolution of AI. As the world stands at the brink of an AI revolution, it becomes increasingly important to understand its nuances and potential implications.</p><p dir="ltr"><a href="https://www.wevolver.com/profile/brainchip" target="_blank" rel="noopener noreferrer">Read more about BrainChip on Wevolver. </a></p>

Navigating AI's Future in a Riveting Podcast Discussion

BrainChip's Deep Dive into AI's Future: A Podcast with Tenstorrent's COO Keith Witek

<p>The winners of the <a href="https://www.tinyml.org/event/tinyml-hackathon-2023-pedestrian-detection/" target="_blank" rel="noopener noreferrer">Pedestrian Safety Challenge Hackathon</a> competition have been announced. The hackathon was a collaboration between the TinyML Foundation the City of San Jose and &nbsp;Sony Semiconductor Solutions America (SSSA). This initiative began in May with the aim of reducing pedestrian-involved accidents, aligning with the city's Vision Zero initiatives.&nbsp;</p><p>The challenge was a response to the increasing number of pedestrian injuries and fatalities due to issues such as distracted driving, distracted walking, and illegal road crossings.</p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="640" height="360" src="https://www.youtube.com/embed/ZhBCtfalcOk?&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"><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></p><p dir="ltr">The competition saw participation from 29 teams globally, including countries like the United States, Germany, Lebanon, Nigeria, and Saudi Arabia. Teams from Silicon Valley and the San Francisco Bay Area also took part. Mark Hanson, Vice President and Head of Marketing for System Solution Business Development at SSSA, expressed his optimism about the innovative solutions presented by the teams and their potential to benefit communities worldwide.</p><p>The<a href="https://leopardimaging.com/" target="_blank" rel="noopener noreferrer"> Leopard Imaging </a>team clinched the first place with their solution that utilized SSSA's AiTrios™ platform and IMX500-enabled hardware. The Neurohm team secured the second place, followed by the team from King Abdullah University of Science and Technology (KAUST) in third place. The KAUST team also received a special Edge Impulse award.</p><p>Leopard Imaging is a global leader in camera design and manufacture, creating state-of-the-art, AI-based cameras for Fortune 500 companies. Their reputation is credited by excellent camera manufacture and camera tuning by experts of the field, and the use of highly precise Active Alignment (AA) machines. Over the years their excellence in building artificially intelligent and robust camera solutions has earned Leopard Imaging many certifications like those for automotive-grade camera manufacture and aviation-grade camera development. Their cameras have spanned all imaginable industries, from autonomous vehicles to cashierless carts, smart street lights, and embedded cameras in drones.</p><p>Read more about the hackathon in our previous article <a href="https://www.wevolver.com/article/revolutionizing-traffic-safety-the-global-tinyml-traffic-hackathon" target="_blank" rel="noopener noreferrer">here</a>.&nbsp;</p>

 Leopard Imaging wins global challenge. 

Winners of the tinyML Traffic Hackathon demonstrate the future of pedestrian safety

<p dir="ltr" id="isPasted">Moore’s Law and distributed cloud computing have enabled artificial intelligence (AI) and machine learning (ML) applications to effectively overcome Von Neumann bottlenecks that once limited data throughput on conventional systems. With enormous amounts of targeted compute power and memory available in the cloud, AI/ML training and inference models continue to increase in both size and sophistication.</p><p dir="ltr">However, cloud-based data centers have created a new set of challenges for AI applications at the edge such as latency, power, and security. Untethering edge AI from the cloud helps address some of these issues—and creates opportunities for the semiconductor industry to design new products with smarter and more independent sensors, devices, and systems.</p><p dir="ltr">For example, autonomous vehicles leverage cloud-free, edge AI learning at high speeds to continuously update and define safety parameters that make it easier for onboard systems to detect anomalous structural vibrations and engine sounds.</p><p dir="ltr">Cloud-free edge AI also enables gesture control with faster response times, allowing doctors and therapists to help people with disabilities interact with sophisticated robotic assistance devices. In the future, field hospitals in disaster zones can deploy medical robots with advanced edge AI capabilities, even if connectivity is limited.</p><p dir="ltr">Lastly, smart farms in remote areas depend on cloud-free AI to help lower food prices by efficiently increasing crop yields with intelligent soil sensors, irrigation systems, and autonomous drones.</p><h2 dir="ltr">The human brain shows us the way</h2><p dir="ltr">Untethering edge AI from the cloud is an important first step to enabling smarter endpoints. However, the semiconductor industry must also acknowledge the inefficiencies of simply scaling down AI hardware at the edge. Indeed, advanced edge applications are already hitting the limits of conventional AI silicon and standard learning models.</p><p dir="ltr">Many chips used in edge applications today are still general-purpose processors such as GPUs that consume approximately 1,000X more power than purpose-built edge silicon. Although some smart devices are equipped with low-power digital signal processors (DSPs), these single-purpose chips typically lack advanced learning and analytics capabilities.</p><p dir="ltr">In recent years, neuromorphic computing has unlocked more efficient architectures and learning models such as incremental and one-shot learning for edge AI. Smart edge silicon that follows the principles of essential AI—doing more with less—now supports a new generation of advanced multimodal and autonomous use cases with independent learning and inference capabilities, faster response times, and a lower power budget.</p><p dir="ltr">These include self-driving cars that personalize cabin settings for individual drivers, automated factories and warehouses, advanced speech and facial recognition applications, and robots that use sophisticated sensors to see, hear, smell, touch, and taste.&nbsp;</p><h2 dir="ltr">The future’s not only bright, it’s essential</h2><p dir="ltr">Edge AI presents both a challenge and opportunity for the semiconductor industry to look well beyond cutting the cord to the cloud and simply scaling down conventional chip architectures. Neuromorphic edge AI silicon is already enabling people to seamlessly interact with smarter devices that independently learn new skills, intelligently anticipate requests, and instantly deliver services.</p><p dir="ltr">Specific strategies to unlocking the full potential of edge AI will undoubtedly vary, which is why it is important to explore a future in which semiconductor companies play a collaborative role in helping to design and implement new neuromorphic architectures.</p><h2 dir="ltr">Edge AI: The Cloud-Free Future is Now Whitepaper</h2><p dir="ltr">Brainchip and the Global Semiconductor Alliance (GSA) have jointly published a white paper titled “Edge AI: The Cloud-Free Future is Now” that discusses:</p><ul><li dir="ltr"><p dir="ltr">Untethering edge AI from the cloud</p></li><li dir="ltr"><p dir="ltr">Overcoming the limits of conventional AI silicon and learning methodologies</p></li><li dir="ltr"><p dir="ltr">Unlocking new architectures and learning models with neuromorphic computing</p></li><li dir="ltr"><p dir="ltr">Supporting autonomous edge AI applications</p></li></ul><p dir="ltr">“We are excited to support the publication of this white paper as it is a call to our industry to work together to address the unique learning and performance requirements of edge AI,” said Brainchip Marketing and Strategy Advisor, Jerome Nadel. “We hope it provokes conversation and collaboration—and ultimately enables effective edge compute to be universally deployable across real world applications such as connected cars, consumer electronics, and industrial IoT.”</p><p dir="ltr"><a href="https://brainchip.com/#whitepaper" target="_blank" rel="noopener noreferrer" class="button-main-action">Click here to read the full white paper.</a></p><h2 dir="ltr">Building collective knowledge</h2><p dir="ltr">Over the next few months, as part of our new series spotlighting Neuromorphic computing, we will share content that explores how neuromorphic edge silicon can mimic the human brain to analyze only essential sensor inputs at the point of acquisition. Specifically, we’ll take a closer look at the four primary design principles of neuromorphic edge silicon, how to efficiently scale and optimize on-chip memory, as well as strategies for leveraging inference and one-shot learning. We’ll also discuss real-world edge AI use cases, including automotive edge learning at high speeds, wine tasting, object detection and classification, and keyword spotting.</p><p dir="ltr">Follow the <a href="https://www.wevolver.com/profile/brainchip" target="_blank" rel="noopener noreferrer">BrainChip</a> Wevolver profile to stay-up-to date.&nbsp;</p>

This is the first article in a new series from BrainChip highlighting their vision and technology. 


Edge AI: Why the cloud-free future is now

<p dir="ltr" id="isPasted"><em>In a recent episode of the <a href="https://www.youtube.com/@BrainChipInc" target="_blank">BrainChip podcas</a>t, Rob Telson, the Vice President of Sales and Marketing at BrainChip, spoke to Michael Azoff, an industry analyst with a focus on AI at the edge. He has 18 years of experience as an analyst and has previously worked hands-on with neural networks. We highlight some of the key discussion points below but encourage you to listen to the </em><a href="https://www.youtube.com/watch?v=xsIWZXlin7I&ab_channel=BrainChipInc" target="_blank"><em>whole episode here.</em></a></p><h2 dir="ltr">A Dive into the Past and Present</h2><p dir="ltr">During the podcast, Azoff recounted his extensive experience with AI, which began in the 1990s. He reminisced about the early days of neuromorphic computing and how it was just taking root. As the conversation progressed, he highlighted the significant milestones, emphasizing the advancements in training neuromorphic architecture-based neural networks. He also acknowledged BrainChip's notable ascent in recent years, pointing to it as evidence of the growing momentum in the neuromorphic field.</p><h3>Neuromorphic's Niche in Edge AI</h3><p dir="ltr">Azoff's recent contributions to Gigaom's reports have centered on Edge AI, a field that emphasizes inferencing at the edge. In his analysis, the Edge AI domain bifurcates into three distinct categories: ultra-low power, low power, and automotive. Intriguingly, neuromorphic architectures have carved a niche for themselves in the ultra-low power segment. Their inherent efficiency and minimal power consumption make them a natural fit for this category.</p><h3>Applications: From Surveillance to Automotive</h3><p dir="ltr">Neuromorphic architectures excel in scenarios characterized by sparse data. For instance, in visual surveillance, where a static scene is occasionally interrupted by noteworthy events, these architectures prove invaluable. Their design allows them to operate in close proximity to sensors, processing data in real-time. The applications are diverse: smart doorbells, earbuds, industrial fault detection, and even monitoring crowd density at train stations. While vision and audio remain the primary focus areas, the potential applications are vast.</p><p dir="ltr">In the automotive sector, a distinction emerges between in-cabin applications and driving functionalities. While the latter, especially autonomous driving, demands robust processing, neuromorphic computing finds its place in the former. In-cabin applications, ranging from infotainment to human-machine communication, can benefit immensely from neuromorphic's capabilities.</p><h2>Consumer Electronics: The Next Frontier</h2><p dir="ltr">As neuromorphic technology matures, its integration into consumer products is anticipated to grow. The allure of low power consumption cannot be overstated. For application developers, this presents a golden opportunity to innovate, pushing the boundaries of what's possible. In a decade, we might witness a paradigm shift where handheld devices and consumer electronics come equipped with neuromorphic processors, either as accelerators or integral components of the system's ecosystem.</p><h3>AI's Societal Impact and the Dream of Understanding the Human Brain</h3><p dir="ltr">Beyond applications, the podcast touched upon a profound topic: the beneficial impact of AI on society. AI's prowess lies in its ability to automate tasks that surpass human capabilities, especially when scale and complexity come into play. The horizon is vast, with the ultimate goal being the enhancement of AI architectures' intelligence, inching closer to the elusive general artificial intelligence.</p><p>Azoff's vision for an AI superpower is both ambitious and profound: to unravel the mysteries of the human brain. If AI can aid in understanding our brain's intricacies, the implications for neuroscience, psychology, and AI itself could be groundbreaking.</p><h3>In Conclusion</h3><p>This episode of BrainChip's podcast offers a panoramic view of neuromorphic computing's past, present, and future. As we stand on the cusp of AI's next revolution, neuromorphic computing emerges as a beacon, guiding the way forward. The journey is just beginning, and the possibilities are boundless.</p><p><a href="https://www.wevolver.com/profile/brainchip" target="_blank">&nbsp;Stay up to date with BrainChip's new content here.</a></p>

In this episode, Rob Telson speaks with Industry Expert, Michael Azoff, about neuromorphic computing in the AI space including edge AI, neural network architectures, sensory-based applications and more.

BrainChip Podcast: Exploring Neuromorphic Computing with Industry Expert Michael Azoff

<p dir="ltr" id="isPasted"><em>In a recent episode of the <a href="https://www.youtube.com/@BrainChipInc" target="_blank" rel="noopener noreferrer">BrainChip podcas</a>t, a select group of the company's top executives gathered to discuss the future trajectory of artificial intelligence (AI) and the pivotal role BrainChip is set to play in this dynamic landscape. We highlight some of the key discussion points below but encourage you to listen to the&nbsp;</em><a href="https://www.youtube.com/watch?v=zUA9Vdd6cE8&t=2s&ab_channel=BrainChipInc" target="_blank"><em>whole episode here.</em></a></p><h2 dir="ltr">Leadership with Vision</h2><p>Rob Telson Vice President, Ecosystem and Partnerships, is the driving force behind BrainChip's global outreach. With a keen understanding of market dynamics and a vision for BrainChip's expansive global presence, Tolson has been instrumental in positioning the company as a leader in the AI industry.</p><p dir="ltr">Peter Van der Made, <span lang="EN-AU">Executive Director, CTO, and Founder</span> of BrainChip, brings a wealth of experience and a visionary approach to the table. His emphasis on the importance of the company's Advanced Research Center in Perth showcases his commitment to pioneering the next wave of AI innovations. Vandermade's insights into the potential of Akida, BrainChip's flagship technology, highlight his forward-thinking approach to AI's future.</p><p>Anil Mankar, the Vice President of Product Development and also a Co-founder, offers a deep dive into the technical intricacies of BrainChip's operations. His insights into the production process, from chip manufacturing to rigorous testing, provide a glimpse into the meticulous steps BrainChip takes to ensure top-tier functionality and performance.</p><p>Lastly, Ken Scarance, the Chief Financial Officer of BrainChip, sheds light on the company's financial endeavours. His discussions on Brain Chips strategic financial initiatives, including capital raising agreements and efforts to bolster its presence in the U.S. capital markets, underscore the company's ambitions for growth and market dominance.</p><h2 dir="ltr"><strong>BrainChip's Evolution: A Glimpse into the Company's Global Strategy&nbsp;</strong></h2><p>To kick off the episode, the team outlines the company’s history, vision and future plans. BrainChip's commitment to being a global leader in the AI industry is evident in its expansive operational presence. With hubs in California, Perth, France, and India, the company has strategically positioned itself in key tech-centric locations. This global footprint not only facilitates diverse collaborations but also ensures that BrainChip remains at the pulse of AI advancements worldwide.</p><h3 dir="ltr">From Research to Production: A Strategic Pivot</h3><p dir="ltr">Historically, BrainChip has been synonymous with cutting-edge research in neuromorphic computing. Their dedication to pushing the boundaries of AI has positioned them as pioneers in the field. However, recognizing the vast commercial potential of their innovations and the industry's shifting dynamics, BrainChip is undergoing a transformation.</p><p dir="ltr">Tolson emphasised this transition in the podcast, noting the company's pivot from being primarily research-driven to adopting a production-centric approach. For engineers, this shift signifies BrainChip's intent to translate their groundbreaking research into tangible, market-ready solutions.</p><h3 dir="ltr">Engaging the Tech Community</h3><p dir="ltr">Understanding the importance of effective communication in the tech world, especially among engineers and developers, Tolson highlighted BrainChip's efforts to foster engagement. The company's podcast series, for instance, is more than just a marketing tool. It's a platform for knowledge sharing, offering insights into Brain Chips offerings, their vision for AI's future, and the technical intricacies that make their solutions stand out. Stay up to date with BrainChip activities by following their <a href="https://www.wevolver.com/profile/brainchip" target="_blank">Wevolver profile.&nbsp;</a></p><h3 dir="ltr">The Advanced Research Center: A Beacon of Innovation</h3><p dir="ltr">Located in Perth, Australia the Advanced Research Center is BrainChip's crown jewel. It's not just a research facility; it's a testament to the company's dedication to pushing the boundaries of what's possible in AI. While many in the industry focus on refining existing deep learning models, BrainChip's centre is already looking beyond, exploring the next frontier of AI innovations.</p><h3 dir="ltr">Akida: The Future of AI Technology</h3><p dir="ltr">Vandermade's enthusiasm was palpable when discussing <a href="https://brainchip.com/akida-generations/" target="_blank">Akida</a>, BrainChip's flagship technology. Akida is not just another chip in the market; it embodies BrainChip's vision for the future of AI. Two of its standout features are its low energy consumption and its on-chip learning capabilities.</p><p dir="ltr">For engineers and tech enthusiasts, these features are significant. The low energy consumption means that Akida is not only efficient but also environmentally conscious, addressing a growing concern in today's tech-driven world. On the other hand, on-chip learning capabilities represent a leap in AI technology, allowing for faster, more efficient processing without the need for constant back-and-forth with centralised data centres.</p><p dir="ltr">In Vandermade's view, Akida is set to redefine the AI industry. Its unique features position it not just as an innovative product but as a transformative solution that could shape the way we think about and implement AI in various applications.</p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3MjA4NDI5MTg2LWFraWRhLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib"></p><h3 dir="ltr">Financial Initiatives and Market Presence</h3><p dir="ltr">To wrap up the podcast, Ken Scarance, BrainChip's Chief Financial Officer, touched upon the company's recent financial endeavours. He highlighted the company's agreement with LDA, aimed at raising capital, and BrainChip's strategic move to enhance its presence in the U.S. capital markets. Additionally, the company is bolstering its investor relations strategy, aiming to foster better communication with stakeholders and educate the market about BrainChips groundbreaking offerings.</p><h3 dir="ltr">Conclusion</h3><p dir="ltr">As BrainChip continues to make strides in the AI domain, the company remains committed to keeping its audience informed and engaged. With a series of events lined up to showcase Akida's capabilities and an unwavering focus on innovation, BrainChip is undoubtedly poised to redefine the boundaries of AI.<a href="https://www.wevolver.com/profile/brainchip" target="_blank" rel="noopener noreferrer">&nbsp;Stay up to date with BrainChip's new content here.</a></p>

BrainChip's leadership shares details on the company's development of the Akida chip, future prospects, and more in this informative podcast. 

BrainChip Podcast Leadership Roundtable: These are Exciting Times

<p id="isPasted">RISC-V is inevitable - it became the mantra of RISC-V, and it's true. But before we see why that is, let’s step back and discuss what RISC-V is and why we should care.</p><p>Back in the early days, computers were large and bulky, taking up entire rooms and making a lot of noise.</p><p>It's incredible to think about how much technology has advanced over the years. Nowadays, we're surrounded by microprocessors that make our lives easier without us even realizing it. For example, when we use our bank cards to pay at the supermarket, we're actually using a full computer that encrypts, signs and verifies our transactions. And our phones contain dozens and dozens of processors that make all the conveniences of modern life possible.</p><h2>What is RISC-V?</h2><p>RISC-V (Reduced Instruction Set Computers) is an open-source Instruction Set Architecture (ISA). You may have heard of other well-known ISAs such as x86, ARM, Power, and MIPS.&nbsp;</p><p>The RISC-V ISA is the common, standardized language between the processor (hardware) and the programs running on it (software). With RISC-V, the processor itself can only do very basic things, such as (conditionally) adding or subtracting two numbers and deciding what to do based on those arithmetic outcomes. It can also repeat those arithmetic operations several times until a certain condition is reached. In fact, there are only 37 instructions in the base. But these are the very basic ingredients needed to describe essentially any problem to the processor.&nbsp;</p><p>We call those commands, instructions.&nbsp;</p><p>By defining the exact meaning (semantic) and spelling (syntax) of those instructions, you can start to build a common understanding between the processor and the software running on it. You can think of the ISA as a dictionary. The ISA of RISC-V is free for anyone to access here. This is incredibly important because today’s software has become so complex that no single company can manage it alone and a common standard ensures interoperability.&nbsp;</p><p>RISC-V came to the right place, by the right people, at the right time.&nbsp;</p><p>David Patterson, a visionary in the field of computer architecture, spearheaded the development of Reduced Instruction Set Computers (RISC) at Berkeley. The project went through five iterations, which ultimately led to the creation of the RISC-V architecture.</p><p>Initially, RISC-V mainly piqued academic curiosity. However, the availability of open-source processor implementations, coupled with rising geopolitical tensions, quickly drew industrial interest as well. From that point, the project experienced a snowball effect gaining momentum and broader adoption.</p><h2>Open source breeds innovation</h2><p>The primary advantage of RISC-V lies in its licensing flexibility. Although the specifications for other ISAs are publicly available, legal constraints prevent you from implementing and selling processor hardware unless you have obtained a license for the ISA specification. With RISC-V, this limitation is removed, offering greater freedom for innovation and commercialization.</p><p>The limits that most ISAs have reduce their use by smaller innovators. For example, the widely used ISA “x86” allows only a handful of licensees. Arm, another well-known provider of processor IP, has an entirely different business model. They are selling processor IPs that can be licensed and integrated into your product. Only the biggest players in the market are able to own an architectural license that allows them to implement their own processors.&nbsp;</p><p>Most notable of these is Apple, which switched its entire product line from Intel x86 processors to its own Arm implementation. Their investment in this endeavor proved to be a wise decision both technically and economically, as it enabled them to distinguish their product line even further and surpass their rivals by a significant margin.</p><p>In the past, designing your own processor and utilizing the expansive software ecosystem was thought to be reserved solely for large corporations. However, that's not entirely true. Alternatives like OpenRISC existed, but they weren't widely adopted because their software ecosystems were underdeveloped, and they were often considered hobbyist projects.</p><p>Another notable benefit of RISC-V is its built-in modularity, which contrasts with ARM's more rigid structure. The RISC-V architecture is designed from the ground up to be modular, allowing for easier customization and innovation. While ARM tightly controls and protects its ISA specification, making it difficult to introduce your own specialized features, RISC-V offers the flexibility to add custom extensions and your “secret sauce.”</p><p>This is particularly advantageous if you aim to target specific market segments and differentiate your product from competitors. Remember those 37 instructions I’ve mentioned? Turns out this is only the very base instruction set. For most applications, you would likely want to include some more specialized instructions such as floating-point support, atomic memory operations, or maybe even scalable vector extensions. You don’t need to, but you can. In the same way, your company could add your own instructions in case those are beneficial for the application in mind. This means you can still leverage the entire software ecosystem that has been and is being built and only add value on top instead of reinventing the wheel. So let’s have a look at some of these advantages.&nbsp;</p><h2>How can RISC-V help start-ups design their own chips?</h2><p>For me, the two most important aspects for a hardware-producing start-up are time-to-market and freedom to innovate.&nbsp;</p><p>Most silicon/hardware start-ups innovate and try to provide value-add in their niche. The processor is a necessary, boring commodity and usually not the main star of the product. This is good and the way it should be. Otherwise, we would all be selling the same processors.&nbsp;</p><p>RISC-V is an absolute game changer for that.&nbsp;</p><p>Do you want to quickly try out an integration idea? Prototype something on an FPGA? Before RISC-V this either meant you needed to use some custom FPGA vendor’s ISA (which you couldn’t send to manufacture for your final product) or start negotiations with the processor vendor of your choice. Negotiations are hard for a small silicon start-up. With RISC-V you can go and grab some core from the internet, do your trials, and start building your software stack. Once happy with the prototype and you want to move forward, there are a plethora of RISC-V IP vendors you can choose from. Choice and market competition are a good thing.</p><p>Expanding on the points raised earlier, the freedom to innovate with RISC-V comes without the risk of architectural lock-in. This has two advantages, first, you are free to switch IP vendors. Your software stack will still work the same because you adhered to an open standard. Second, you can provide your own extensions on top of the main RISC-V standards, and the company can provide very concentrated value-add without the need to maintain or develop commodity.&nbsp;</p><h3>Let’s have a look at what we’ve done at Axelera.&nbsp;</h3><p>At the heart of our Metis AI Processing Unit, there are four AI Cores. Each AI core provides a 512x512 matrix-vector multiplication (MVM) in-memory compute array and a vector datapath that operates on streams of data.&nbsp;</p><p>To provide a generic solution that can keep up with the fast evolution in the field of neural networks we kept the datapath control as flexible as possible. We aim to push as much as possible into low-level driver software where we can innovate, correct, and adapt throughout the product’s life cycle. Therefore, each AI Core has a dedicated RISC-V, application-class core, that has full control over the datapath unit. A 5th system controller provides SoC management and workload dispatch to the individual AI cores. We’ve confidently chosen the RISC-V ISA because we could start virtual prototyping and building our software stack on open and free software far before we had done any IP negotiations. Furthermore, we knew that the IP landscape would provide us with sufficient competitive choices for our needs.</p><p>&nbsp;In roughly a year, we went from concept/FPGA prototype to tape-out, and in just a couple of days after having received the chips back, we could run the first end-to-end neural networks on our lab benches. Taking the limited resources and this aggressive time schedule into account it was clear that we’ll needed to buy proven IP without long negotiations to kick-start our developments.&nbsp;</p><p>Choosing to proceed with RISC-V ensured that we could continue to innovate in future generations and further distinguish our offerings.</p><p>So, is it all love, peace, and harmony? Not exactly. Like anything that's still a work in progress. RISC-V has its own set of hiccups and curveballs.</p><p>An ISA is mainly about instructions, but a processor is part of a bigger system that includes memory, peripherals, and other processors. This broader setup, often called a platform, isn't covered in the main ISA spec. The RISC-V Foundation aims to standardize these aspects too.</p><p>However, details matter. Things like atomic handling in multi-hart systems may only be standardized for specific platforms. Also, some IP providers might have legacy elements because they developed solutions before RISC-V standards were set. Standards are scattered, so you have to keep an eye out. And given the rapid growth, it's tough to stay updated on all the ongoing activities.</p><h2>Looking ahead</h2><p>Remember those room-filling mainframes and how technology has continuously shrunk down to fit our pockets and wrists? Just like those evolutionary leaps, RISC-V aims to be a cornerstone in the next phase of computational progress.</p><p>In an ideal future, RISC-V will be as "boring" as any well-established technology—boring in the sense that it's reliable, stable, and so embedded in our daily lives that we take it for granted. The long-term vision is for software to run seamlessly on RISC-V-based processors without users having to worry about compatibility issues.</p><p>In the short term, however, there's still much to be done. While RISC-V is showing promise in deeply embedded systems like smartcards and IoT devices, achieving the same level of support and efficiency as ARM and x86 in more user-centric applications—like your phone or computer—is an ongoing project. Industry trends, as evidenced by companies like SiFive and Ventana and initiatives like the RISE project, suggest a competitive and collaborative future for RISC-V. From a research standpoint, its open nature makes it a fertile ground for innovation across the entire hardware-to-software stack.</p><p>There's a palpable sense of momentum behind RISC-V, with heavyweight stakeholders and top-tier companies pooling their resources and expertise. Far from slowing down, the ecosystem around RISC-V is only gaining speed, setting it on a course to become as ubiquitous and "boring" as the microprocessors that have come to define modern life.&nbsp;</p><p>So, looking at where things are going, ignoring RISC-V would be like missing the boat when we switched from those giant mainframes to desktop PCs. It's a game-changer, and you really don't want to be left behind.</p>

RISC-V Evolution: From Early Computing to Tomorrow's Tech Promise 

Harnessing the RISC-V Wave: The Future is Now

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