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Machine Learning at the Edge

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NANO-CHIPS 2030

Part of the book series: The Frontiers Collection ((FRONTCOLL))

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.

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  1. KU Leuven, Leuven, Belgium

    Marian Verhelst

  2. Stanford University, Stanford, USA

    Boris Murmann

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  1. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

    Boris Murmann

  2. Sindelfingen, Baden-Württemberg, Germany

    Bernd Hoefflinger

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Verhelst, M., Murmann, B. (2020). Machine Learning at the Edge. In: Murmann, B., Hoefflinger, B. (eds) NANO-CHIPS 2030. The Frontiers Collection. Springer, Cham. https://doi.org/10.1007/978-3-030-18338-7_18

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