Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons

2025/06/24 by Wu, Dengyu, Chen, Jiechen, Poor, H. Vincent +2
#FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE)

paper · doi:10.48550/arxiv.2506.20015

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.

Citations

• Symbol Detection Using an Integrate-and-Fire Time Encoding Receiver
• Direct Training Needs Regularisation: Anytime Optimal Inference Spiking Neural Network
• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning
• Balanced Resonate-and-Fire Neurons
• Efficient Video and Audio processing with Loihi 2
• Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guarantees
• Neuromorphic Integrated Sensing and Communications
• Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference
• Efficient Neuromorphic Signal Processing with Loihi 2
• Training Spiking Neural Networks Using Lessons From Deep Learning
• Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook
• Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks
• DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures
• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
• Non-negative matrix factorization with sparseness constraints

Related

• End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge\n Artificial Intelligence
• An Asynchronous Mixed-Signal Resonate-and-Fire Neuron
• Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference
• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning
• Accurate Mapping of RNNs on Neuromorphic Hardware with Adaptive Spiking Neurons