Machine Learning at the Edge

Abstract

Machine learning algorithms have been successfully deployed in cloud-centric applications and on computationally powerful digital platforms such as high-end FPGAs and GPUs. As this rise of machine learning applications continues, some of these algorithms must move “closer to the sensor,” thereby eliminating the latency of cloud access and providing a scalable solution that avoids the large energy cost per bit transmitted through the network. This chapter reviews state-of-the-art approaches and trends for low-energy machine learning inference and training at the edge. It covers dataflow, architecture and circuit design aspects of fully digital processors and mixed analog/digital implementations targeting the microwatts and milliwatts range.