From algorithms to devices: Enabling machine learning through ultra-low-power VLSI mixed-signal array processing

Machine learning and related statistical signal processing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things. As such, architectures embedding adaptive and learning algorithms on-chip are oft-ignored by the design community and pre...

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Bibliographic Details
Published in2017 IEEE Custom Integrated Circuits Conference (CICC) pp. 1 - 9
Main Authors Joshi, Siddharth, Chul Kim, Sohmyung Ha, Cauwenberghs, Gert
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2017
Subjects
Digital signal processing
Dynamic range
Gain
Parallel processing
Signal processing algorithms
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Summary:Machine learning and related statistical signal processing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things. As such, architectures embedding adaptive and learning algorithms on-chip are oft-ignored by the design community and present a new set of design trade-offs. This review focuses on efficient implementation of mixed-signal matrix-vector multiplication as a central computational primitive enabling machine learning and statistical signal processing, with specific examples in spatial filtering for adaptive beamforming. We describe adaptive algorithms amenable for efficient implementation with such primitives in the presence of noise and analog variability. We also briefly highlight current trends in high-density integration in emerging memory device technologies and their use in highdimensional adaptive computing.
ISSN:2152-3630
DOI:10.1109/CICC.2017.7993650