Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons

TL;DR

This paper introduces resonate-and-fire neurons in a neuromorphic wireless split computing system, enabling efficient spectral feature extraction directly from time-series signals, reducing energy consumption while maintaining accuracy.

Contribution

It proposes a novel RF neuron model for wireless split computing that captures spectral features without spectral pre-processing, improving energy efficiency.

Findings

Achieves comparable accuracy to traditional models in audio and modulation classification.

Reduces spike rates and energy consumption significantly during inference and communication.

Demonstrates effective spectral feature extraction using RF neurons with oscillatory dynamics.

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.