Hardware-algorithm collaborative computing with photonic spiking neuron chip based on an integrated Fabry–Perot laser with a saturable absorber

Posted on 2023-01-25 - 00:31
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 propose and fabricate a photonic spiking neuron chip based on an integrated Fabry–Pérot laser with a saturable absorber (FP-SA). The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, refractory period, and cascadability are experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we propose time-multiplexed spike encoding to realize 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 capability in performing classification tasks with supervised learning algorithm, which paves the way for multi-layer PSNN for solving complex tasks. © 2022 Optica Publishing Group

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Xiang, Shuiying; Shi, Yuechun; Guo, Xingxing; ZHANG, YAHUI; Wang, Hongji; ZHENG, DIAN ZHUANG; et al. (2023): Hardware-algorithm collaborative computing with photonic spiking neuron chip based on an integrated Fabry–Perot laser with a saturable absorber. Optica Publishing Group. Collection. https://doi.org/10.6084/m9.figshare.c.6053699.v2
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AUTHORS (10)

Shuiying Xiang
Yuechun Shi
Xingxing Guo
YAHUI ZHANG
Hongji Wang
DIAN ZHUANG ZHENG
Ziwei Song
Yanan Han
Shuang Gao
ShiHao Zhao
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