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Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks

Author

Listed:
  • Rohit Abraham John

    (Nanyang Technological University)

  • Jyotibdha Acharya

    (Nanyang Technological University
    Nanyang Technological University)

  • Chao Zhu

    (Nanyang Technological University)

  • Abhijith Surendran

    (Nanyang Technological University)

  • Sumon Kumar Bose

    (Nanyang Technological University)

  • Apoorva Chaturvedi

    (Nanyang Technological University)

  • Nidhi Tiwari

    (Nanyang Technological University)

  • Yang Gao

    (Nanyang Technological University)

  • Yongmin He

    (Nanyang Technological University)

  • Keke K. Zhang

    (Nanyang Technological University)

  • Manzhang Xu

    (Nanyang Technological University)

  • Wei Lin Leong

    (Nanyang Technological University)

  • Zheng Liu

    (Nanyang Technological University)

  • Arindam Basu

    (Nanyang Technological University)

  • Nripan Mathews

    (Nanyang Technological University
    Nanyang Technological University)

Abstract

Shallow feed-forward networks are incapable of addressing complex tasks such as natural language processing that require learning of temporal signals. To address these requirements, we need deep neuromorphic architectures with recurrent connections such as deep recurrent neural networks. However, the training of such networks demand very high precision of weights, excellent conductance linearity and low write-noise- not satisfied by current memristive implementations. Inspired from optogenetics, here we report a neuromorphic computing platform comprised of photo-excitable neuristors capable of in-memory computations across 980 addressable states with a high signal-to-noise ratio of 77. The large linear dynamic range, low write noise and selective excitability allows high fidelity opto-electronic transfer of weights with a two-shot write scheme, while electrical in-memory inference provides energy efficiency. This method enables implementing a memristive deep recurrent neural network with twelve trainable layers with more than a million parameters to recognize spoken commands with >90% accuracy.

Suggested Citation

  • Rohit Abraham John & Jyotibdha Acharya & Chao Zhu & Abhijith Surendran & Sumon Kumar Bose & Apoorva Chaturvedi & Nidhi Tiwari & Yang Gao & Yongmin He & Keke K. Zhang & Manzhang Xu & Wei Lin Leong & Zh, 2020. "Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16985-0
    DOI: 10.1038/s41467-020-16985-0
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    Cited by:

    1. 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.
    2. Gang Wu & Mohamed Abid & Mohamed Zerara & Jiung Cho & Miri Choi & Cormac Ó Coileáin & Kuan-Ming Hung & Ching-Ray Chang & Igor V. Shvets & Han-Chun Wu, 2024. "Miniaturized spectrometer with intrinsic long-term image memory," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. Ik-Jyae Kim & Min-Kyu Kim & Jang-Sik Lee, 2023. "Highly-scaled and fully-integrated 3-dimensional ferroelectric transistor array for hardware implementation of neural networks," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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