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Bayesian neural network with unified entropy source and synapse weights using 3D 16-layer Fe-diode array

Author

Listed:
  • Yuanquan Huang

    (Chinese Academy of Sciences
    Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Qiqiao Wu

    (Fudan University
    Zhangjiang Laboratory)

  • Tiancheng Gong

    (Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Jianguo Yang

    (Chinese Academy of Sciences
    Chinese Academy of Sciences
    Zhangjiang Laboratory)

  • Qing Luo

    (Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Ming Liu

    (Chinese Academy of Sciences
    Chinese Academy of Sciences
    Fudan University)

Abstract

Edge artificial intelligence systems require higher frequency due to intensive computational demands, while most traditional entropy sources decay with frequency. This work shows the physical properties of the Fe-diode devices are ideal for edge systems with high frequencies and dramatic temperature changes. The noise density of Fe-diode can be modified by the amplitude of the read voltage and remains stable at high frequencies and temperature fluctuations. A Bayesian neural network with Fe-diode devices is experimentally implemented in high-speed, high-density silicon-based chips. This hierarchical Bayesian neural network is demonstrated on 3D 16-layer Fe-diode array based on unified entropy source and 4-state synapse. Properties including high area efficiency, wide working temperature range, low energy in-situ training, high recognition accuracy are finally achieved.

Suggested Citation

  • Yuanquan Huang & Qiqiao Wu & Tiancheng Gong & Jianguo Yang & Qing Luo & Ming Liu, 2025. "Bayesian neural network with unified entropy source and synapse weights using 3D 16-layer Fe-diode array," Nature Communications, Nature, vol. 16(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63302-8
    DOI: 10.1038/s41467-025-63302-8
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