Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons
Abstract
Neuromorphic computing offers an energy-efficient alternative to conventional deep learning accelerators for real-time time-series processing. 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.
Cite
@article{arxiv.2506.20015,
title = {Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons},
author = {Dengyu Wu and Jiechen Chen and H. Vincent Poor and Bipin Rajendran and Osvaldo Simeone},
journal= {arXiv preprint arXiv:2506.20015},
year = {2025}
}