Neuromorphic computing offers an energy-efficient alternative to conventional deep learning accelerators, particularly for real-time processing of time-series data. However, many edge applications, such as wireless sensing and audio recognition, generate streaming signals with rich spectral features that are not effectively captured by conventional leaky integrate-and-fire (LIF) spiking neurons. This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons with oscillatory dynamics to process time-domain signals directly, eliminating the need for costly spectral pre-processing. By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity. This temporal sparsity translates into significant savings in both computation and transmission energy. Assuming an OFDM-based analog wireless interface for spike transmission, we present a complete system design and evaluate its performance on audio classification and modulation classification tasks. Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs, while substantially reducing spike rates and total energy consumption during inference and communication.

翻译：神经形态计算为传统深度学习加速器提供了一种节能替代方案，尤其适用于时间序列数据的实时处理。然而，许多边缘应用，如无线感知和音频识别，会产生具有丰富频谱特征的流式信号，而传统的漏泄积分-点火（LIF）脉冲神经元无法有效捕捉这些特征。本文研究了一种无线分割计算架构，该架构采用具有振荡动态特性的谐振-点火（RF）神经元直接处理时域信号，从而消除了昂贵的频谱预处理需求。通过在可调频率上谐振，RF神经元在保持低脉冲活动的同时提取时间局部化频谱特征。这种时间稀疏性转化为计算和传输能量方面的显著节省。假设采用基于OFDM的模拟无线接口进行脉冲传输，我们提出了完整的系统设计，并在音频分类和调制分类任务上评估其性能。实验结果表明，所提出的RF-SNN架构在实现与传统LIF-SNN和ANN相当精度的同时，在推理和通信过程中显著降低了脉冲率和总能耗。