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Neuromorphic computing with multi-memristive synapses

Author

Listed:
  • Irem Boybat

    (IBM Research - Zurich
    Microelectronic Systems Laboratory, EPFL, Bldg ELD)

  • Manuel Le Gallo

    (IBM Research - Zurich)

  • S. R. Nandakumar

    (IBM Research - Zurich
    New Jersey Institute of Technology)

  • Timoleon Moraitis

    (IBM Research - Zurich)

  • Thomas Parnell

    (IBM Research - Zurich)

  • Tomas Tuma

    (IBM Research - Zurich)

  • Bipin Rajendran

    (New Jersey Institute of Technology)

  • Yusuf Leblebici

    (Microelectronic Systems Laboratory, EPFL, Bldg ELD)

  • Abu Sebastian

    (IBM Research - Zurich)

  • Evangelos Eleftheriou

    (IBM Research - Zurich)

Abstract

Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently represent the synaptic weights in artificial neural networks. However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be challenging. To address this, we present a multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme. We focus on phase change memory devices, develop a comprehensive model and demonstrate via simulations the effectiveness of the concept for both spiking and non-spiking neural networks. Moreover, we present experimental results involving over a million phase change memory devices for unsupervised learning of temporal correlations using a spiking neural network. The work presents a significant step towards the realization of large-scale and energy-efficient neuromorphic computing systems.

Suggested Citation

  • Irem Boybat & Manuel Le Gallo & S. R. Nandakumar & Timoleon Moraitis & Thomas Parnell & Tomas Tuma & Bipin Rajendran & Yusuf Leblebici & Abu Sebastian & Evangelos Eleftheriou, 2018. "Neuromorphic computing with multi-memristive synapses," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04933-y
    DOI: 10.1038/s41467-018-04933-y
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    Cited by:

    1. Kwon, Osung & Kim, Sungjun & Agudov, Nikolay & Krichigin, Alexey & Mikhaylov, Alexey & Grimaudo, Roberto & Valenti, Davide & Spagnolo, Bernardo, 2022. "Non-volatile memory characteristics of a Ti/HfO2/Pt synaptic device with a crossbar array structure," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    2. Dong Gue Roe & Dong Hae Ho & Yoon Young Choi & Young Jin Choi & Seongchan Kim & Sae Byeok Jo & Moon Sung Kang & Jong-Hyun Ahn & Jeong Ho Cho, 2023. "Humanlike spontaneous motion coordination of robotic fingers through spatial multi-input spike signal multiplexing," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    3. Rohit Abraham John & Yiğit Demirağ & Yevhen Shynkarenko & Yuliia Berezovska & Natacha Ohannessian & Melika Payvand & Peng Zeng & Maryna I. Bodnarchuk & Frank Krumeich & Gökhan Kara & Ivan Shorubalko &, 2022. "Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    4. Wang, Xueqin & Yu, Dong & Li, Tianyu & Jia, Ya, 2023. "Logistic stochastic resonance in the Hodgkin–Huxley neuronal system under electromagnetic induction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    5. Parit, Aditya Kuber & Yadav, Mani Shankar & Gupta, Avinash Kumar & Mikhaylov, Alexey & Rawat, Brajesh, 2021. "Design and modeling of niobium oxide-tantalum oxide based self-selective memristor for large-scale crossbar memory," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    6. Rui Yuan & Qingxi Duan & Pek Jun Tiw & Ge Li & Zhuojian Xiao & Zhaokun Jing & Ke Yang & Chang Liu & Chen Ge & Ru Huang & Yuchao Yang, 2022. "A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    7. Simon Wintersteller & Olesya Yarema & Dhananjeya Kumaar & Florian M. Schenk & Olga V. Safonova & Paula M. Abdala & Vanessa Wood & Maksym Yarema, 2024. "Unravelling the amorphous structure and crystallization mechanism of GeTe phase change memory materials," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    8. Koryazhkina, M.N. & Filatov, D.O. & Shishmakova, V.A. & Shenina, M.E. & Belov, A.I. & Antonov, I.N. & Kotomina, V.E. & Mikhaylov, A.N. & Gorshkov, O.N. & Agudov, N.V. & Guarcello, C. & Carollo, A. & S, 2022. "Resistive state relaxation time in ZrO2(Y)-based memristive devices under the influence of external noise," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    9. Chiara Bartolozzi & Giacomo Indiveri & Elisa Donati, 2022. "Embodied neuromorphic intelligence," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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