Conversion of a single-layer ANN to photonic SNN for pattern recognition  

作　　者：Yanan HAN Shuiying XIANG Tianrui ZHANG Yahui ZHANG Xingxing GUO Yuechun SHI 

机构地区：[1]State Key Laboratory of Integrated Service Networks,State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology,Xidian University,Xi’an 710071,China [2]Yongjiang Laboratory,Ningbo 315202,China

出　　处：《Science China(Information Sciences)》2024年第1期257-266,共10页中国科学（信息科学）（英文版）

基　　金：supported in part by National Key Research and Development Program of China(Grant Nos.2021YFB2801900,2021YFB2801901,2021YFB2801902,2021YFB2801904);National Natural Science Foundation of China(Grant Nos.61974177,61674119);National Outstanding Youth Science Fund Project of National Natural Science Foundation of China(Grant No.62022062);Fundamental Research Funds for the Central Universities(Grant No.JB210114)。

摘　　要：This work presents a complete conversion scheme for photonic spiking neural networks(SNNs).We verified that the output of an artificial neural network(ANN)trained with the simulated optical activation function can be directly converted into the spike rate of a photonic spiking neuron model.To reveal the feasibility of hardware implementation,we considered the effects of different bit precisions of data and weight,noise level,and bias current mismatch on the converted results.The proposed scheme was evaluated using the Deterding vowel,IRIS,TIDIGITS,and MNIST datasets for pattern recognition,and achieved mean accuracies of 95.80%,98.67%,96.19%,and 92.33%,respectively.The proposed scheme can convert an ANN into a photonic SNN with almost no precision loss,and the performance was comparable to that of an ANN trained with the rectified linear unit function.The proposed scheme can enable the high-performance implementation of photonic SNNs.

关 键 词：photonic SNN CONVERSION optical computing pattern recognition artificial neural network 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]