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An adaptive synaptic array using Fowler–Nordheim dynamic analog memory

Author

Listed:
  • Darshit Mehta

    (Washington University in St. Louis)

  • Mustafizur Rahman

    (Washington University in St. Louis)

  • Kenji Aono

    (Washington University in St. Louis)

  • Shantanu Chakrabartty

    (Washington University in St. Louis
    Washington University in St. Louis)

Abstract

In this paper we present an adaptive synaptic array that can be used to improve the energy-efficiency of training machine learning (ML) systems. The synaptic array comprises of an ensemble of analog memory elements, each of which is a micro-scale dynamical system in its own right, storing information in its temporal state trajectory. The state trajectories are then modulated by a system level learning algorithm such that the ensemble trajectory is guided towards the optimal solution. We show that the extrinsic energy required for state trajectory modulation can be matched to the dynamics of neural network learning which leads to a significant reduction in energy-dissipated for memory updates during ML training. Thus, the proposed synapse array could have significant implications in addressing the energy-efficiency imbalance between the training and the inference phases observed in artificial intelligence (AI) systems.

Suggested Citation

  • Darshit Mehta & Mustafizur Rahman & Kenji Aono & Shantanu Chakrabartty, 2022. "An adaptive synaptic array using Fowler–Nordheim dynamic analog memory," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29320-6
    DOI: 10.1038/s41467-022-29320-6
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    References listed on IDEAS

    as
    1. Darshit Mehta & Kenji Aono & Shantanu Chakrabartty, 2020. "A self-powered analog sensor-data-logging device based on Fowler-Nordheim dynamical systems," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    2. Stefano Ambrogio & Pritish Narayanan & Hsinyu Tsai & Robert M. Shelby & Irem Boybat & Carmelo Nolfo & Severin Sidler & Massimo Giordano & Martina Bodini & Nathan C. P. Farinha & Benjamin Killeen & Chr, 2018. "Equivalent-accuracy accelerated neural-network training using analogue memory," Nature, Nature, vol. 558(7708), pages 60-67, June.
    3. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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