Community-aware sparse topology design for efficient spiking neural networks.

Abstract

Spiking Neural Networks (SNNs) offer a promising pathway toward energy-efficient neuromorphic computing due to their event-driven computation and sparse spike-based communication. However, most existing SNN architectures are derived from dense Artificial Neural Networks (ANNs) and do not explicitly exploit the role of network topology in learning dynamics. In this work, we propose a community-aware sparse topology design framework for graph-based SNNs. Using seven distinct community detection algorithms (KMeans, Spectral Clustering, Fast Greedy, Louvain, Leiden, Infomap, and Small-World), we systematically compare how different modular organizations influence convergence speed, classification accuracy, and energy consumption under strictly controlled conditions (64 neurons, 92% sparsity, ≈ 10 communities, T = 4 time steps). Experimental results on MNIST and CIFAR-10 reveal a dataset-dependent trade-off. On simple, low-noise MNIST, fine-grained methods like Infomap achieve the highest accuracy (99.67%). On the more complex CIFAR-10, coarse and noise-robust methods (Louvain, KMeans, Small-World) perform best (≈ 92.96% accuracy), slightly outperforming fine-grained algorithms (≈ 90.8%). Notably, all community-driven topologies converge dramatically faster than conventional SNNs (27-44 epochs vs. 100-300 epochs). Despite using twice as many neurons as the baseline TANet-Tiny, our sparse modular architectures maintain the same inference energy (≈ 1.2 mJ per sample) thanks to higher sparsity (92% vs. ≈80%) and structured connectivity, halving the energy per neuron. These findings challenge the prevailing assumption that network size or sparsity alone is sufficient, demonstrating that how sparse connections are organized - the graph topology - critically influences learning efficiency, accuracy, and energy consumption. Our framework provides practical guidelines for dataset-aware community detection in neuromorphic system design.