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

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. (c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

Photonic neuromorphic computing is a promising approach for low-latency and energy-efficient computing systems. A photonic spiking neural network (PSNN) utilizes brain-like spatiotemporal processing for high-performance neuromorphic computing. However, the nonlinear computation of a PSNN remains challenging. In this study, a photonic spiking neuron chip based on an integrated Fabry-Perot laser with a saturable absorber (FP-SA) is proposed and fabricated. It demonstrates the nonlinear dynamics of a neuron-like system and serves as a fundamental building block for constructing the PSNN hardware. Time-multiplexed temporal spike encoding is proposed to surpass the hardware integration scale limit and enable functional PSNNs. Experimental results show that PSNNs with single or cascaded photonic spiking neurons can perform classification tasks using a supervised learning algorithm, paving the way for multilayer PSNNs to handle complex tasks.