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Trainable hardware for dynamical computing using error backpropagation through physical media

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  • Michiel Hermans

    (OPERA photonique, Université Libre de Bruxelles)

  • Michaël Burm

    (Ghent University)

  • Thomas Van Vaerenbergh

    (Ghent University)

  • Joni Dambre

    (Ghent University)

  • Peter Bienstman

    (Ghent University)

Abstract

Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation—a crucial step for tuning such systems towards a specific task—can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.

Suggested Citation

  • Michiel Hermans & Michaël Burm & Thomas Van Vaerenbergh & Joni Dambre & Peter Bienstman, 2015. "Trainable hardware for dynamical computing using error backpropagation through physical media," Nature Communications, Nature, vol. 6(1), pages 1-8, November.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms7729
    DOI: 10.1038/ncomms7729
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    Cited by:

    1. Elena Goi & Steffen Schoenhardt & Min Gu, 2022. "Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

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