aiqbench analysis
This research advances neuromorphic computing by introducing resonate-and-fire neurons that natively process time-domain signals for wireless edge applications, bypassing energy-intensive spectral pre-processing. The architecture demonstrates comparable accuracy to conventional models while significantly reducing spike rates and total power consumption during inference and communication. It represents a meaningful step toward efficient, real-time AI processing in resource-constrained environments like audio recognition and sensing networks.
Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons
arXiv:2506.20015v2 Announce Type: replace Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Information Theory (cs.IT); Neural and Evolutionary Computing (cs.NE)
arXiv:2506.20015 [cs.LG]
(or arXiv:2506.20015v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2506.20015 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 260 words
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