Hardware-algorithm collaborative computing with photonic spiking neuron chip based on an integrated Fabry–Perot laser with a saturable absorber
Shuiying Xiang, Yuechun Shi, Xingxing Guo, Yahui Zhang, Hongji Wang, Dianzhuang Zheng, Ziwei Song, Yanan Han, Shuang Gao, Shihao Zhao, Biling Gu, Hailing Wang, Xiaojun Zhu, Lianping Hou, Xiangfei Chen, Wanhua Zheng, Xiaohua Ma, Yue Hao
Abstract
Photonic neuromorphic computing has emerged as a promising approach to building a low-latency and energy-efficient non-von Neuman computing system. A photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of a PSNN remains a significant challenge. Here, we propose and fabricate a photonic spiking neuron chip based on an integrated Fabry–Perot laser with a saturable absorber (FP-SA). The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, a refractory period, inhibitory behavior and cascadability are experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we propose time-multiplexed temporal spike encoding to realize a functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons are experimentally demonstrated to realize hardware-algorithm collaborative computing, showing the capability to perform classification tasks with a supervised learning algorithm, which paves the way for a multilayer PSNN that can handle complex tasks.