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An ultra energy-efficient hardware platform for neuromorphic computing enabled by 2D-TMD tunnel-FETs

Author

Listed:
  • Arnab Pal

    (University of California)

  • Zichun Chai

    (University of California)

  • Junkai Jiang

    (University of California)

  • Wei Cao

    (University of California)

  • Mike Davies

    (Intel Labs)

  • Vivek De

    (Intel Labs)

  • Kaustav Banerjee

    (University of California)

Abstract

Brain-like energy-efficient computing has remained elusive for neuromorphic (NM) circuits and hardware platform implementations despite decades of research. In this work we reveal the opportunity to significantly improve the energy efficiency of digital neuromorphic hardware by introducing NM circuits employing two-dimensional (2D) transition metal dichalcogenide (TMD) layered channel material-based tunnel-field-effect transistors (TFETs). Our novel leaky-integrate-fire (LIF) based digital NM circuit along with its Hebbian learning circuitry operates at a wide range of supply voltages, frequencies, and activity factors, enabling two orders of magnitude higher energy-efficient computing that is difficult to achieve with conventional material and/or device platforms, specifically the silicon-based 7 nm low-standby-power FinFET technology. Our innovative 2D-TFET based NM circuit paves the way toward brain-like energy-efficient computing that can unleash major transformations in future AI and data analytics platforms.

Suggested Citation

  • Arnab Pal & Zichun Chai & Junkai Jiang & Wei Cao & Mike Davies & Vivek De & Kaustav Banerjee, 2024. "An ultra energy-efficient hardware platform for neuromorphic computing enabled by 2D-TMD tunnel-FETs," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46397-3
    DOI: 10.1038/s41467-024-46397-3
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    References listed on IDEAS

    as
    1. Wei Cao & Huiming Bu & Maud Vinet & Min Cao & Shinichi Takagi & Sungwoo Hwang & Tahir Ghani & Kaustav Banerjee, 2023. "The future transistors," Nature, Nature, vol. 620(7974), pages 501-515, August.
    2. Wei Cao & Huiming Bu & Maud Vinet & Min Cao & Shinichi Takagi & Sungwoo Hwang & Tahir Ghani & Kaustav Banerjee, 2023. "Publisher Correction: The future transistors," Nature, Nature, vol. 621(7979), pages 43-43, September.
    3. M. Prezioso & F. Merrikh-Bayat & B. D. Hoskins & G. C. Adam & K. K. Likharev & D. B. Strukov, 2015. "Training and operation of an integrated neuromorphic network based on metal-oxide memristors," Nature, Nature, vol. 521(7550), pages 61-64, May.
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