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Probabilistic computing with NbOx metal-insulator transition-based self-oscillatory pbit

Author

Listed:
  • Hakseung Rhee

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Gwangmin Kim

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Hanchan Song

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Woojoon Park

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Do Hoon Kim

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Jae Hyun In

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Younghyun Lee

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Kyung Min Kim

    (Korea Advanced Institute of Science and Technology (KAIST))

Abstract

Energy-based computing is a promising approach for addressing the rising demand for solving NP-hard problems across diverse domains, including logistics, artificial intelligence, cryptography, and optimization. Probabilistic computing utilizing pbits, which can be manufactured using the semiconductor process and seamlessly integrated with conventional processing units, stands out as an efficient candidate to meet these demands. Here, we propose a novel pbit unit using an NbOx volatile memristor-based oscillator capable of generating probabilistic bits in a self-clocking manner. The noise-induced metal-insulator transition causes the probabilistic behavior, which can be effectively modeled using a multi-noise-induced stochastic process around the metal-insulator transition temperature. We demonstrate a memristive Boltzmann machine based on our proposed pbit and validate its feasibility by solving NP-hard problems. Furthermore, we propose a streamlined operation methodology that considers the autocorrelation of individual bits, enabling energy-efficient and high-performance probabilistic computing.

Suggested Citation

  • Hakseung Rhee & Gwangmin Kim & Hanchan Song & Woojoon Park & Do Hoon Kim & Jae Hyun In & Younghyun Lee & Kyung Min Kim, 2023. "Probabilistic computing with NbOx metal-insulator transition-based self-oscillatory pbit," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43085-6
    DOI: 10.1038/s41467-023-43085-6
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    References listed on IDEAS

    as
    1. Kyung Seok Woo & Jaehyun Kim & Janguk Han & Woohyun Kim & Yoon Ho Jang & Cheol Seong Hwang, 2022. "Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    2. Gwangmin Kim & Jae Hyun In & Young Seok Kim & Hakseung Rhee & Woojoon Park & Hanchan Song & Juseong Park & Kyung Min Kim, 2021. "Self-clocking fast and variation tolerant true random number generator based on a stochastic mott memristor," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    3. Suhas Kumar & Ziwen Wang & Noraica Davila & Niru Kumari & Kate J. Norris & Xiaopeng Huang & John Paul Strachan & David Vine & A.L. David Kilcoyne & Yoshio Nishi & R. Stanley Williams, 2017. "Physical origins of current and temperature controlled negative differential resistances in NbO2," Nature Communications, Nature, vol. 8(1), pages 1-6, December.
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