AI 中文总结
本文针对五种感知模态和多款边缘设备开展脉冲神经网络基准测试，发现其优势依赖于感知模态，无线感知领域尤为有利，还提供了用于可复现基准测试的开源框架。
AI 中文摘要
边缘计算系统需要在严格的能量和内存约束下支持多样化的感知工作负载，因此催生了面向部署场景的模型选择需求。脉冲神经网络（SNN）是传统人工神经网络（ANN）的一种有前景的替代方案，但关于其在何种场景下、因何具备实用优势的系统证据仍较为有限。本文针对五种感知模态和多款边缘设备开展SNN基准测试，在一致的训练与部署协议下系统评估脉冲编码、神经元模型和网络拓扑。研究发现SNN的优势强烈依赖于模态：在多数工作负载中SNN性能与ANN大致相当，但无线感知成为SNN的特别有利领域；频域与特征空间分析进一步解释了该结果，表明脉冲动力学天然契合无线信号的谱-时间结构。部署分析还显示SNN的优势并非单一维度，能量增益常伴随模态相关的系统成本。最后，本文提供了一个用于可复现基准测试与部署分析的开源框架，为新兴边缘与神经形态计算平台上的算法-软件-硬件协同设计提供实用基础。
英文摘要
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.
