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

Yuanquan Huang, Qiqiao Wu, Tiancheng Gong (), Jianguo Yang (), Qing Luo () and Ming Liu
Additional contact information
Yuanquan Huang: Chinese Academy of Sciences
Qiqiao Wu: Fudan University
Tiancheng Gong: Chinese Academy of Sciences
Jianguo Yang: Chinese Academy of Sciences
Qing Luo: Chinese Academy of Sciences
Ming Liu: Chinese Academy of Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-8

Abstract: 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.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-63302-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63302-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-63302-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().