Photonic Spiking Graph Neural Network for Energy-Efficient Structured Data Processing

TL;DR

This paper introduces a novel photonic spiking graph neural network (PSGNN) that combines GNNs, spiking neurons, and photonic hardware to enable ultra-fast, energy-efficient processing of structured data, validated through simulations and hardware experiments.

Contribution

The work presents the first photonic spiking GNN architecture with hardware-software co-optimization and demonstrates its effectiveness on benchmark datasets with high accuracy and efficiency.

Findings

Achieved 97% test accuracy on PubMed dataset

Demonstrated 97 ps inference latency and 280 GOPS/W energy efficiency

Built a silicon photonics MZI array for hardware validation

Abstract

Photonic computing shows great potential for signal processing and artificial intelligence (AI) acceleration due to its ultra-high speed, low energy consumption, and inherent parallelism. Existing photonic computing research has mainly focused on convolutional neural networks (CNNs) and fully connected neural networks (FCNNs), which are well suited for tasks such as image classification and object detection but face limitations in handling graph-structured data. Graph neural networks (GNNs) are specifically designed to model complex relational structures. In this work, we propose a photonic spiking graph neural network (PSGNN) architecture that integrates the structural modeling capability of GNNs, the temporal dynamics of spiking neurons, and the parallel computing advantages of photonic hardware. Through hardware-software co-optimization, a bias-term simulation method tailored for photonic chips is implemented using feature-dimension expansion, enabling effective network training. Experiments on the KarateClub and PubMed datasets achieve training accuracies of 100 percent (92 +/- 2 percent) and test accuracies of 97 percent (90 +/- 1 percent). A silicon photonics 4 x 4 Mach-Zehnder interferometer (MZI) array is further constructed for hardware validation, achieving a test accuracy of 93 percent. The system demonstrates an inference latency of 97 ps, with an energy efficiency of 280 GOPS/W and a computational density of 52 GOPS/mm^2. These results highlight the potential of PSGNN for structured-data processing applications.