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.

翻译：图神经网络已成为深度学习的一个专门分支，旨在解决对象间成对关系至关重要的问题。最新进展利用图卷积神经网络提取图结构中的特征。尽管取得了令人鼓舞的结果，这些方法在实际应用中仍面临特征稀疏带来的挑战，导致资源利用效率低下。近年研究借鉴哺乳动物大脑的启发，采用脉冲神经网络建模和学习图结构。然而，这些方法局限于传统的冯·诺依曼计算系统，仍存在硬件效率低下的问题。在本研究中，我们提出了专为Loihi 2设计的全神经形态实现的脉冲图神经网络。我们使用Lava贝叶斯优化（一种与神经形态计算架构兼容的新型超参数优化系统）来优化网络参数。我们展示了结合神经形态贝叶斯优化的方法在使用固定精度脉冲神经元进行引文图分类时的性能优势。结果表明，整数精度的Loihi 2兼容脉冲神经网络在执行引文图分类时，能够达到与现有浮点实现相当的准确率。