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Hardware-algorithm collaborative computing with photonic spiking neuron chip based on integrated Fabry-P\'erot laser with saturable absorber

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Version 2 2023-06-08, 12:54
Version 1 2023-01-12, 15:30
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posted on 2023-06-08, 12:54 authored by 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
Photonic neuromorphic computing has emerged as a promising avenue toward building a low-latency and energy-efficient non-von-Neuman computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of PSNN remains a significant challenging. Here, we proposed and fabricated a photonic spiking neuron chip based on an integrated Fabry-P\'erot laser with a saturable absorber (FP-SA) for the first time. The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, refractory period, and cascadability were experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we proposed time-multiplexed spike encoding to realize functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons were experimentally demonstrated to realize hardware-algorithm collaborative computing, showing capability in performing classification tasks with supervised learning algorithm, which paves the way for multi-layer PSNN for solving complex tasks.

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