Transductive Spiking Graph Neural Networks for Loihi
This work addresses hardware inefficiencies for researchers and practitioners in neuromorphic computing and graph learning, though it is incremental as it adapts existing methods to a new platform.
The study tackled the problem of inefficient resource utilization in graph neural networks for real-world applications by implementing a fully neuromorphic spiking graph neural network on Loihi 2 hardware, achieving comparable accuracy to floating-point implementations in citation graph classification.
Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements utilize graph convolutional neural networks to extract features within graph structures. Despite promising results, these methods face challenges in real-world applications due to sparse features, resulting in inefficient resource utilization. Recent studies draw inspiration from the mammalian brain and employ spiking neural networks to model and learn graph structures. However, these approaches are limited to traditional Von Neumann-based computing systems, which still face hardware inefficiencies. In this study, we present a fully neuromorphic implementation of spiking graph neural networks designed for Loihi 2. We optimize network parameters using Lava Bayesian Optimization, a novel hyperparameter optimization system compatible with neuromorphic computing architectures. We showcase the performance benefits of combining neuromorphic Bayesian optimization with our approach for citation graph classification using fixed-precision spiking neurons. Our results demonstrate the capability of integer-precision, Loihi 2 compatible spiking neural networks in performing citation graph classification with comparable accuracy to existing floating point implementations.