Bayesian neural network with unified entropy source and synapse weights using 3D 16-layer Fe-diode array.
basic_science · Level V
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- Record sourced from PubMed, PMID 40877241.
- Also identified by DOI 10.1038/s41467-025-63302-8 and PMC identifier 12394416.
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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.