Multilayer optical-electrical spiking neural network with sparse spike event for speech recognition based on a fabricated DFB-SA laser

Spiking neural networks (SNNs) are gaining increasing attention in artificial intelligence and machine learning due to their high power efficiency, which stems from their event-driven nature and sparse spiking activity. Optical platforms offer significant speed improvements for neural network computations. However, they face challenges in achieving low-threshold nonlinear activation, a crucial requirement for high expressivity in neural networks. Achieving nonlinear activation within the optical domain is essential to minimize redundant optical-electrical conversions. In this work, we propose the implementation of a multi-layer SNN with extremely sparse spike events for speech recognition on an optical-electrical platform. Notably, each neuron in SNN emits an average of 0.4 spikes with the time-to-first spike encoding strategy, which is about four times fewer spikes than in other spiking neural networks. Moreover, a self-fabricated distributed feedback laser with a saturable absorber (DFB-SA) laser is employed to perform nonlinear activation, yielding promising accuracy results with an accuracy of 90.5%. This study demonstrates the feasibility of nonlinear computation within the optical domain for processing a benchmark speech recognition dataset with sparse spike events, and paves the way for the application of photonic SNNs in more complex tasks.