Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons

• URL: http://arxiv.org/abs/2506.20015v1
• Date: Tue, 24 Jun 2025 21:14:59 GMT
• Title: Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons
• Authors: Dengyu Wu, Jiechen Chen, H. Vincent Poor, Bipin Rajendran, Osvaldo Simeone,
• Abstract summary: This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons to process time-domain signals directly.<n>By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity.<n> Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs.
• Score: 69.73249913506042
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Fractional Spike Differential Equations Neural Network with Efficient Adjoint Parameters Training [63.3991315762955]
  Spiking Neural Networks (SNNs) draw inspiration from biological neurons to create realistic models for brain-like computation.<n>Most existing SNNs assume a single time constant for neuronal membrane voltage dynamics, modeled by first-order ordinary differential equations (ODEs) with Markovian characteristics.<n>We propose the Fractional SPIKE Differential Equation neural network (fspikeDE), which captures long-term dependencies in membrane voltage and spike trains through fractional-order dynamics.
  arXiv  Detail & Related papers  (2025-07-22T18:20:56Z)
• TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting [27.91825785119938]
  Spiking Neural Networks (SNNs) offer a promising, biologically inspired approach for processing data for time series forecasting.<n>We introduce the Temporal Leaky Segment Integrate-and-Fire model, featuring a dual-compartment architecture.<n> Experimental results show that TS-LIF outperforms traditional SNNs in time series forecasting.
  arXiv  Detail & Related papers  (2025-03-07T03:06:21Z)
• Range and Angle Estimation with Spiking Neural Resonators for FMCW Radar [16.91912935835324]
  Automotive radar systems face the challenge of managing high sampling rates and large data bandwidth.<n>Neuromorphic computing offers promising solutions because of its inherent energy efficiency and parallel processing capacity.<n>This research presents a novel spiking neuron model for signal processing of frequency-modulated continuous wave (FMCW) radars.
  arXiv  Detail & Related papers  (2025-03-02T13:51:03Z)
• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks.<n> embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption.<n> split computing - where an SNN is partitioned across two devices - is a promising solution.<n>This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics [2.707548544084083]
  Spiking Neural Networks (SNNs) struggle to model skeleton dynamics, leading to suboptimal solutions.<n>We propose Signal-SGN (Spiking Graph Convolutional Network), which utilizes the temporal dimension of skeleton sequences as the spike time steps.<n> Experiments across three large-scale datasets reveal Signal-SGN exceeding state-of-the-art SNN-based methods in accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2024-08-03T07:47:16Z)
• Spatial Annealing for Efficient Few-shot Neural Rendering [73.49548565633123]
  We introduce an accurate and efficient few-shot neural rendering method named textbfSpatial textbfAnnealing regularized textbfNeRF (textbfSANeRF)<n>By adding merely one line of code, SANeRF delivers superior rendering quality and much faster reconstruction speed compared to current few-shot neural rendering methods.
  arXiv  Detail & Related papers  (2024-06-12T02:48:52Z)
• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
  This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
  arXiv  Detail & Related papers  (2024-04-02T10:19:04Z)
• DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor [2.9175555050594975]
  We present a brain-inspired platform for prototyping real-time event-based Spiking Neural Networks (SNNs) The system proposed supports the direct emulation of dynamic and realistic neural processing phenomena such as short-term plasticity, NMDA gating, AMPA diffusion, homeostasis, spike frequency adaptation, conductance-based dendritic compartments and spike transmission delays. The flexibility to emulate different biologically plausible neural networks, and the chip's ability to monitor both population and single neuron signals in real-time, allow to develop and validate complex models of neural processing for both basic research and edge-computing applications.
  arXiv  Detail & Related papers  (2023-10-01T03:48:16Z)
• SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World [19.696976370895907]
  We propose SpikingNeRF, which aligns the temporal dimension of spiking neural networks (SNNs) with the radiance rays. The computation turns into a spike-based, multiplication-free manner, reducing energy consumption and making high-quality 3D rendering accessible to neuromorphic hardware.
  arXiv  Detail & Related papers  (2023-09-20T01:04:57Z)
• Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network [68.43026108936029]
  We propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment. We implement this algorithm in a real-time robotic system with a microphone array. The experiment results show a mean error azimuth of 13 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.
  arXiv  Detail & Related papers  (2020-07-07T08:22:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.