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A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing

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Listed:
  • S. Bianchi

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IUNET
    Infineon Technologies)

  • I. Muñoz-Martin

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IUNET
    Infineon Technologies)

  • E. Covi

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IUNET
    NaMLab gGmbH)

  • A. Bricalli

    (Weebit Nano)

  • G. Piccolboni

    (Weebit Nano)

  • A. Regev

    (Weebit Nano)

  • G. Molas

    (Weebit Nano)

  • J. F. Nodin

    (Univ. Grenoble Alpes, CEA, Leti, F-38000)

  • F. Andrieu

    (Univ. Grenoble Alpes, CEA, Leti, F-38000)

  • D. Ielmini

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IUNET)

Abstract

Neurobiological systems continually interact with the surrounding environment to refine their behaviour toward the best possible reward. Achieving such learning by experience is one of the main challenges of artificial intelligence, but currently it is hindered by the lack of hardware capable of plastic adaptation. Here, we propose a bio-inspired recurrent neural network, mastered by a digital system on chip with resistive-switching synaptic arrays of memory devices, which exploits homeostatic Hebbian learning for improved efficiency. All the results are discussed experimentally and theoretically, proposing a conceptual framework for benchmarking the main outcomes in terms of accuracy and resilience. To test the proposed architecture for reinforcement learning tasks, we study the autonomous exploration of continually evolving environments and verify the results for the Mars rover navigation. We also show that, compared to conventional deep learning techniques, our in-memory hardware has the potential to achieve a significant boost in speed and power-saving.

Suggested Citation

  • S. Bianchi & I. Muñoz-Martin & E. Covi & A. Bricalli & G. Piccolboni & A. Regev & G. Molas & J. F. Nodin & F. Andrieu & D. Ielmini, 2023. "A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37097-5
    DOI: 10.1038/s41467-023-37097-5
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    References listed on IDEAS

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    1. M. Prezioso & M. R. Mahmoodi & F. Merrikh Bayat & H. Nili & H. Kim & A. Vincent & D. B. Strukov, 2018. "Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    2. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    3. M. R. Mahmoodi & M. Prezioso & D. B. Strukov, 2019. "Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
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    2. Wu, Fuqiang & Kang, Ting & Shao, Yan & Wang, Qingyun, 2023. "Stability of Hopfield neural network with resistive and magnetic coupling," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).

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