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

• DOI: 10.29026/oes.2023.230021
• Publication Date: 2023-11-15T03:21:40Z
• Authors: Yanan Han; ; Shuiying Xiang; Ziwei Song; Shuang Gao; Xingxing Guo; Yahui Zhang; Yuechun Shi; Xiangfei Chen; Yue Hao
• Keywords: Neuromorphic engineering; Robustness

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