Hardware Implementation of Multi-Layer Photonic Spiking Neural Network With Three Cascaded Photonic Spiking Neurons

作者：Xingxing Guo, Shuiying Xiang, Yahui Zhang, Ziwei Song, Yanan Han, Biling Gu, Dianzhuang Zheng, Xiangfei Chen, Yuechun Shi, Yue Hao · 发表于：Journal of Lightwave Technology · 年份：2023 · DOI：10.1109/jlt.2023.3287647 · 被引用次数：6 · 研究领域：Neural Networks and Reservoir Computing、Advanced Memory and Neural Computing、Optical Network Technologies

Photonic technologies offer great prospects for novel ultrafast, energy-efficient and hardware friendly neuromorphic computing platforms. Moreover, Photonic spiking neural network (PSNN) emerged as a promising approach toward building a low-latency and energy-efficient non-von-Neuman computing system is of particular interest. In this article, a fabricated Fabry–Pérot laser with saturable absorber (FP-SA) is employed as the photonic spiking neuron of the PSNN, and a multi-layer PSNN with such three cascaded photonic spiking neurons is proposed. The cascadability of the proposed multi-layer PSNN is demonstrated experimentally. It is found that the performance of the pattern recognition task using the hardware-algorithm collaborative computing is improved in the multi-layer PSNN. Besides, the nonlinear neuron-like dynamics including temporal integration and threshold in such multi-layer PSNN are also experimentally investigated, and better nonlinear neuron-like dynamics can be achieved. Such proposed multi-layer PSNN potentially opens up the prospect of employing simple hardware structure and cooperating with algorithm design to realize a photonic depth neural network to solve more complex problems.