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Optimised weight programming for analogue memory-based deep neural networks

Author

Listed:
  • Charles Mackin

    (IBM Research–Almaden)

  • Malte J. Rasch

    (IBM Research–Yorktown Heights)

  • An Chen

    (IBM Research–Almaden)

  • Jonathan Timcheck

    (Stanford University)

  • Robert L. Bruce

    (IBM Research–Yorktown Heights)

  • Ning Li

    (IBM Research–Yorktown Heights)

  • Pritish Narayanan

    (IBM Research–Almaden)

  • Stefano Ambrogio

    (IBM Research–Almaden)

  • Manuel Gallo

    (IBM Research–Zurich)

  • S. R. Nandakumar

    (IBM Research–Zurich)

  • Andrea Fasoli

    (IBM Research–Almaden)

  • Jose Luquin

    (IBM Research–Almaden)

  • Alexander Friz

    (IBM Research–Almaden)

  • Abu Sebastian

    (IBM Research–Zurich)

  • Hsinyu Tsai

    (IBM Research–Almaden)

  • Geoffrey W. Burr

    (IBM Research–Almaden)

Abstract

Analogue memory-based deep neural networks provide energy-efficiency and per-area throughput gains relative to state-of-the-art digital counterparts such as graphics processing units. Recent advances focus largely on hardware-aware algorithmic training and improvements to circuits, architectures, and memory devices. Optimal translation of software-trained weights into analogue hardware weights—given the plethora of complex memory non-idealities—represents an equally important task. We report a generalised computational framework that automates the crafting of complex weight programming strategies to minimise accuracy degradations during inference, particularly over time. The framework is agnostic to network structure and generalises well across recurrent, convolutional, and transformer neural networks. As a highly flexible numerical heuristic, the approach accommodates arbitrary device-level complexity, making it potentially relevant for a variety of analogue memories. By quantifying the limit of achievable inference accuracy, it also enables analogue memory-based deep neural network accelerators to reach their full inference potential.

Suggested Citation

  • Charles Mackin & Malte J. Rasch & An Chen & Jonathan Timcheck & Robert L. Bruce & Ning Li & Pritish Narayanan & Stefano Ambrogio & Manuel Gallo & S. R. Nandakumar & Andrea Fasoli & Jose Luquin & Alexa, 2022. "Optimised weight programming for analogue memory-based deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31405-1
    DOI: 10.1038/s41467-022-31405-1
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    References listed on IDEAS

    as
    1. Vinay Joshi & Manuel Le Gallo & Simon Haefeli & Irem Boybat & S. R. Nandakumar & Christophe Piveteau & Martino Dazzi & Bipin Rajendran & Abu Sebastian & Evangelos Eleftheriou, 2020. "Accurate deep neural network inference using computational phase-change memory," Nature Communications, Nature, vol. 11(1), pages 1-13, 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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    Cited by:

    1. Djohan Bonnet & Tifenn Hirtzlin & Atreya Majumdar & Thomas Dalgaty & Eduardo Esmanhotto & Valentina Meli & Niccolo Castellani & Simon Martin & Jean-François Nodin & Guillaume Bourgeois & Jean-Michel P, 2023. "Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Malte J. Rasch & Charles Mackin & Manuel Gallo & An Chen & Andrea Fasoli & Frédéric Odermatt & Ning Li & S. R. Nandakumar & Pritish Narayanan & Hsinyu Tsai & Geoffrey W. Burr & Abu Sebastian & Vijay N, 2023. "Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

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