用振荡神经元直接处理时序信号，省去频谱预处理，降低能耗。
Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons
- 采用可调频振荡的共振放电神经元，直接处理时间域信号
- 相比传统模型，脉冲率降低，推理与通信总能耗显著减少
- 适合无线传感、音频识别等实时时序数据场景
类脑计算为实时处理时序数据提供了节能方案，但传统漏电积分-放电（LIF）脉冲神经元难以有效捕捉无线传感、音频识别等应用中流式信号的丰富频谱特征。本文提出一种基于共振放电（RF）神经元的无线分层计算架构，通过振荡动力学直接处理时域信号，避免昂贵的频谱预处理。RF神经元可调谐共振频率，提取局部频谱特征，同时保持低脉冲活动。这种时间稀疏性大幅降低了计算与传输能耗。基于OFDM模拟无线接口，构建完整系统并应用于音频分类与调制分类任务。实验表明，所提RF-SNN在精度上媲美传统LIF-SNN与全连接神经网络（ANN），同时显著降低脉冲率与推理及通信总能耗。
原文摘要 · Abstract (English)
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.
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