Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons
This work addresses energy efficiency for edge applications like wireless sensing and audio recognition, though it appears incremental as it builds on existing neuromorphic and split computing concepts.
The paper tackled the inefficiency of conventional spiking neurons in processing streaming signals with spectral features by proposing a wireless split computing architecture using resonate-and-fire neurons, which achieved comparable accuracy to existing methods while significantly reducing spike rates and total energy consumption.
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.