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, Ming Liu
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. Edge AI systems require high-frequency, temperature-stable entropy sources, which traditional sources fail to provide. Here, the authors experimentally demonstrate a 3D 16-layer Fe-diode array that achieves high efficiency, low energy consumption, and high recognition accuracy.