Edge AI—An Industry View

Abstract

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Edge AI encompasses the technologies at the intersection of machine learning (ML) and embedded systems, enabling the development of intelligent applications on devices with minimal power consumption. These devices, although constrained by limited memory and computational resources, utilize sensors to interact with the physical environment and make decisions using ML algorithms. Edge AI thrives on the synergy between ML architectures and deployment platforms, as the design of one profoundly influences the efficiency and functionality of the other. This keynote paper delves into the rationale for employing microcontrollers and neural processing units (NPUs) designed for constrained use-cases as target platforms for Edge AI, explores the necessity of running ML locally, and highlights the opportunities and challenges these systems pose. It describes the main applications of Edge AI and provides references to a practical use case.