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 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