Hardware-aware Lightweight Photonic Spiking Neural Network for Pattern Classification
Abstract
There exists a significant scale gap between photonic neural network integrated chips and neural networks, which hinders the deployment and application of photonic neural network. Here, we propose hardware-aware lightweight spiking neural networks (SNNs) architecture tailored to our photonic neuromorphic chips, and conducts hardware-software collaborative computing for solving patter classification tasks. Here, we employed a simplified Mach-Zehnder interferometer (MZI) mesh for performing linear computation, and 16-channel distributed feedback lasers with saturable absorber (DFB-SA) array for performing nonlinear spike activation. Both photonic neuromorphic chips based on the MZI mesh and DFB-SA array were designed, optimized and fabricated. Furthermore, we propose a lightweight spiking neural network (SNN) with discrete cosine transform to reduce input dimension and match the input/output ports number of the photonic neuromorphic chips. We demonstrated an end-to-end inference of an entire layer of the lightweight photonic SNN. The hardware-software collaborative inference accuracy is 90% and 80.5% for MNIST and Fashion-MNIST datasets, respectively. The energy efficiency is 1.39 TOPS/W for the MZI mesh, and is 987.65 GOPS/W for the DFB-SA array. The lightweight architecture and experimental demonstration address the challenge of scale mismatch between the photonic chip and SNN, paving the way for the hardware deployment of photonic SNNs.
Cite
@article{arxiv.2512.00419,
title = {Hardware-aware Lightweight Photonic Spiking Neural Network for Pattern Classification},
author = {Shuiying Xiang and Yahui Zhang and Shangxuan Shi and Haowen Zhao and Dianzhuang Zheng and Xingxing Guo and Yanan Han and Ye Tian and Liyue Zhang and Yuechun Shi and Yue Hao},
journal= {arXiv preprint arXiv:2512.00419},
year = {2025}
}