Wevolver | Edge AI Articles

Four Metrics You Must Consider When Developing TinyML Systems

Article 3 of Bringing Intelligence to the Edge Series: Balancing the critical metrics of accuracy, power consumption, latency, and memory requirements is key to unlocking the potential of Tiny Machine Learning (TinyML) in low-power microcontrollers and edge computing.

Revolution of Endpoint AI in Embedded Vision Applications

Article 2 of Bringing Intelligence to the Edge Series: Advancements in AI and embedded vision technologies are revolutionizing various industries, enabling real-time decision-making, enhancing security, and facilitating automation in various applications.

Implementing AI in IoT Endpoints: Balancing Performance, Feasibility, and Data Constraints

Article 1 of Bringing Intelligence to the Edge Series: With the introduction of AI, IoT devices can become more intelligent and less reliant on external systems— but not without trade-offs in performance and cost. Understanding how to make that decision is key.

Bringing Intelligence to the Edge Series

Introducing the Bringing Intelligence to the Edge Series: Exploring how Artificial Intelligence is moving to embedded systems, transforming technology across various applications.

Building a Smarter Future: Exploring MCU Integration in Edge AI

Exploring the integration and impact of microcontrollers and Edge AI across various industries.

Implementing Smart Voice Control in Appliances: A Comprehensive Guide

Article #2 of Spotlight on Innovations in Edge Computing and Machine Learning: Edge AI techniques such as Keyword Spotting can turn an ordinary device into a smart appliance.

Decoding Transformers on Edge Devices

Bert Moons, Director of System Architecture at AXELERA AI discusses bottlenecks in transformer inference for generative AI.

Harnessing AI Platforms for Rapid Development and Deployment of Predictive Maintenance Systems in Industrial Applications

In this article, we will look at the challenges that engineers encounter when deploying a predictive maintenance system and how edge AI platforms like Edge Impulse can help factories optimize and deploy predictive maintenance models on industrial edge devices.

Spotlight on Innovations in Edge Computing and Machine Learning: Predictive Maintenance

Introducing a brand new series featuring innovative projects related to edge computing and machine learning. The first article presents a predictive maintenance solution utilizing the Nordic Thingy:91, monitoring machine health through anomaly detection with a TinyML model.