Benchmarking spiking neural networks across sensing modalities on edge devices

2026-08-27

AI Summary

研究通过在多种传感模式和边缘设备上系统评估SNN，发现其优势取决于具体应用场景，特别是在无线传感领域，并提供了开源框架支持进一步研究。

Abstract

Edge computing systems need to support diverse sensing workloads under tight energy and memory constraints, thereby motivating deployment-aware model selection. Spiking neural networks (SNNs) are a promising alternative to conventional artificial neural networks (ANNs), yet systematic evidence for when and why they provide practical advantages remains limited. Here, we present a benchmark of SNNs across five sensing modalities and multiple edge devices, systematically evaluating spike encoding, neuron models, and network topologies under consistent training and deployment protocols. We find that SNN advantages are strongly modality-dependent: while SNNs achieve performance broadly comparable to ANNs across most workloads, wireless sensing emerges as a particularly favorable domain. Frequency-domain and feature-space analyses further explain this result by showing that spiking dynamics naturally align with the spectral-temporal structure of wireless signals. Our deployment analysis further shows that SNN advantages are not one-dimensional, with energy gains often accompanied by modality-dependent system costs. Finally, we provide an open-source framework for reproducible benchmarking and deployment profiling, offering a practical foundation for algorithm-software-hardware co-design on emerging edge and neuromorphic computing platforms.

Problem

• edge computing
• spiking neural networks
• sensing modalities
• energy constraints
• memory constraints

Innovation

• Spiking Neural Networks
• Edge Computing
• Sensing Modalities
• Deployment-aware Model Selection
• Open-source Framework