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Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search

Author

Listed:
  • Ruibin Mao

    (The University of Hong Kong)

  • Bo Wen

    (The University of Hong Kong)

  • Arman Kazemi

    (Hewlett Packard Labs, Hewlett Packard Enterprise
    University of Notre Dame)

  • Yahui Zhao

    (The University of Hong Kong)

  • Ann Franchesca Laguna

    (University of Notre Dame
    De La Salle University)

  • Rui Lin

    (The University of Hong Kong)

  • Ngai Wong

    (The University of Hong Kong)

  • Michael Niemier

    (University of Notre Dame)

  • X. Sharon Hu

    (University of Notre Dame)

  • Xia Sheng

    (Hewlett Packard Labs, Hewlett Packard Enterprise)

  • Catherine E. Graves

    (Hewlett Packard Labs, Hewlett Packard Enterprise)

  • John Paul Strachan

    (Peter Grünberg Institut (PGI-14), Forschungszentrum Jülich GmbH
    RWTH Aachen University)

  • Can Li

    (The University of Hong Kong)

Abstract

Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory-augmented neural networks have been proposed to achieve the goal, but the memory module must be stored in off-chip memory, heavily limiting the practical use. In this work, we experimentally validated that all different structures in the memory-augmented neural network can be implemented in a fully integrated memristive crossbar platform with an accuracy that closely matches digital hardware. The successful demonstration is supported by implementing new functions in crossbars, including the crossbar-based content-addressable memory and locality sensitive hashing exploiting the intrinsic stochasticity of memristor devices. Simulations show that such an implementation can be efficiently scaled up for one-shot learning on more complex tasks. The successful demonstration paves the way for practical on-device lifelong learning and opens possibilities for novel attention-based algorithms that were not possible in conventional hardware.

Suggested Citation

  • Ruibin Mao & Bo Wen & Arman Kazemi & Yahui Zhao & Ann Franchesca Laguna & Rui Lin & Ngai Wong & Michael Niemier & X. Sharon Hu & Xia Sheng & Catherine E. Graves & John Paul Strachan & Can Li, 2022. "Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33629-7
    DOI: 10.1038/s41467-022-33629-7
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Yijun Li & Jianshi Tang & Bin Gao & Jian Yao & Anjunyi Fan & Bonan Yan & Yuchao Yang & Yue Xi & Yuankun Li & Jiaming Li & Wen Sun & Yiwei Du & Zhengwu Liu & Qingtian Zhang & Song Qiu & Qingwen Li & He, 2023. "Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

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