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CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning

Author

Listed:
  • Nihal Sanjay Singh

    (University of California Santa Barbara)

  • Keito Kobayashi

    (University of California Santa Barbara
    Tohoku University
    Tohoku University)

  • Qixuan Cao

    (University of California Santa Barbara)

  • Kemal Selcuk

    (University of California Santa Barbara)

  • Tianrui Hu

    (University of California Santa Barbara)

  • Shaila Niazi

    (University of California Santa Barbara)

  • Navid Anjum Aadit

    (University of California Santa Barbara)

  • Shun Kanai

    (Tohoku University
    Tohoku University
    Tohoku University
    Tohoku University)

  • Hideo Ohno

    (Tohoku University
    Tohoku University
    Tohoku University
    Tohoku University)

  • Shunsuke Fukami

    (Tohoku University
    Tohoku University
    Tohoku University
    Tohoku University)

  • Kerem Y. Camsari

    (University of California Santa Barbara)

Abstract

Extending Moore’s law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs can perform probabilistic inference and learning by leveraging the algorithmic update-order-invariance of Gibbs sampling. We show how the stochasticity of sMTJs can augment low-quality random number generators (RNG). Detailed transistor-level comparisons reveal that sMTJ-based p-bits can replace up to 10,000 CMOS transistors while dissipating two orders of magnitude less energy. Integrated versions of our approach can advance probabilistic computing involving deep Boltzmann machines and other energy-based learning algorithms with extremely high throughput and energy efficiency.

Suggested Citation

  • Nihal Sanjay Singh & Keito Kobayashi & Qixuan Cao & Kemal Selcuk & Tianrui Hu & Shaila Niazi & Navid Anjum Aadit & Shun Kanai & Hideo Ohno & Shunsuke Fukami & Kerem Y. Camsari, 2024. "CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46645-6
    DOI: 10.1038/s41467-024-46645-6
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    References listed on IDEAS

    as
    1. Takuya Funatsu & Shun Kanai & Jun’ichi Ieda & Shunsuke Fukami & Hideo Ohno, 2022. "Local bifurcation with spin-transfer torque in superparamagnetic tunnel junctions," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    2. 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.
    3. William A. Borders & Ahmed Z. Pervaiz & Shunsuke Fukami & Kerem Y. Camsari & Hideo Ohno & Supriyo Datta, 2019. "Integer factorization using stochastic magnetic tunnel junctions," Nature, Nature, vol. 573(7774), pages 390-393, September.
    4. Lennart Dabelow & Masahito Ueda, 2022. "Three learning stages and accuracy–efficiency tradeoff of restricted Boltzmann machines," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
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    Cited by:

    1. John Daniel & Zheng Sun & Xuejian Zhang & Yuanqiu Tan & Neil Dilley & Zhihong Chen & Joerg Appenzeller, 2024. "Experimental demonstration of an on-chip p-bit core based on stochastic magnetic tunnel junctions and 2D MoS2 transistors," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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