Pattern recognition in multi-synaptic photonic spiking neural networks based on a DFB-SA chip  

作　　者：Yanan Han Shuiying Xiang Ziwei Song Shuang Gao Xingxing Guo Yahui Zhang Yuechun Shi Xiangfei Chen Yue Hao 

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 [3]Key Laboratory of Intelligent Optical Sensing and Manipulation,Ministry of Education,the National Laboratory of Solid State Microstructures,the College of Engineering and Applied Sciences,Institute of Optical Communication Engineering,Nanjing University,Nanjing 210023,China

出　　处：《Opto-Electronic Science》2023年第9期1-10,共10页光电科学（英文）

基　　金：supports from the National Key Research and Development Program of China (Nos.2021YFB2801900,2021YFB2801901,2021YFB2801902,2021YFB2801903,2021YFB2801904);the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (No.62022062);the National Natural Science Foundation of China (No.61974177);the Fundamental Research Funds for the Central Universities (No.QTZX23041).

摘　　要：Spiking neural networks(SNNs)utilize brain-like spatiotemporal spike encoding for simulating brain functions.Photonic SNN offers an ultrahigh speed and power efficiency platform for implementing high-performance neuromorphic computing.Here,we proposed a multi-synaptic photonic SNN,combining the modified remote supervised learning with delayweight co-training to achieve pattern classification.The impact of multi-synaptic connections and the robustness of the network were investigated through numerical simulations.In addition,the collaborative computing of algorithm and hardware was demonstrated based on a fabricated integrated distributed feedback laser with a saturable absorber(DFB-SA),where 10 different noisy digital patterns were successfully classified.A functional photonic SNN that far exceeds the scale limit of hardware integration was achieved based on time-division multiplexing,demonstrating the capability of hardware-algorithm co-computation.

关 键 词：photonic spiking neural network fabricated DFB-SA laser chip multi-synaptic connection optical computing 

分 类 号：TN40[电子电信—微电子学与固体电子学] TP183[自动化与计算机技术—控制理论与控制工程]