mlsanityData to 7 Oct 2026

Neuromorphic Wireless Split Computing with Multi-Level Spikes

arXiv 2411.04728 ↗First posted 7 Nov 2024cs.LG cs.IT cs.NEcited by 3

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Abstract (arXiv)

Inspired by biological processes, neuromorphic computing leverages spiking neural networks (SNNs) to perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have shown that embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption. To scale neuromorphic computing to larger workloads, split computing - where an SNN is partitioned across two devices - is a promising solution. In such architectures, the device hosting the initial layers must transmit information about the spikes generated by its output neurons to the second device. This establishes a trade-off between the benefits of multi-level spikes, which carry additional payload information, and the communication resources required for transmitting extra bits between devices. This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs. We propose digital and analog modulation schemes for an orthogonal frequency division multiplexing (OFDM) radio interface to enable efficient communication. Simulation and experimental results using software-defined radios reveal performance improvements achieved by multi-level SNN models and provide insights into the optimal payload size as a function of the connection quality between the transmitter and receiver.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 30 of its 66 references that are in the map. First posted on arXiv 7 Nov 2024.

Builds on

30 of 66 references.

References · 30

  1. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,031
  2. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 340
  3. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 192
  4. 2018Long short-term memory and learning-to-learn in networks of spiking neuronscited by 179
  5. 2022Sionna: An Open-Source Library for Next-Generation Physical Layer Researchcited by 143
  6. 2023Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networkscited by 116
  7. 2021Efficient Neuromorphic Signal Processing with Loihi 2cited by 90
  8. 2024Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detectioncited by 65
  9. 2023Ternary Spike: Learning Ternary Spikes for Spiking Neural Networkscited by 36
  10. 2023Efficient Video and Audio processing with Loihi 2cited by 28
  11. 2021Federated Learning with Spiking Neural Networkscited by 22
  12. 2022An Introduction to Quantum Machine Learning for Engineerscited by 22
18 more
  1. 2024SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Modelscited by 19
  2. 2019Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligencecited by 16
  3. 2022Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inferencecited by 16
  4. 2023Spiking Neural Network for Ultra-low-latency and High-accurate Object Detectioncited by 14
  5. 2024Roadmap to Neuromorphic Computing with Emerging Technologiescited by 12
  6. 2024LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Modelcited by 10
  7. 2022Neuromorphic Integrated Sensing and Communicationscited by 9
  8. 2023Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guaranteescited by 8
  9. 2020Split Computing for Complex Object Detectors: Challenges and Preliminary Resultscited by 7
  10. 2020End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligencecited by 7
  11. 2024Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinningcited by 7
  12. 2024Neuromorphic In-Context Learning for Energy-Efficient MIMO Symbol Detectioncited by 6
  13. 2021A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network from Convolutional Neural Networkcited by 5
  14. 2023An Asynchronous Wireless Network for Capturing Event-Driven Data from Large Populations of Autonomous Sensorscited by 4
  15. 2023Calibration-Aware Bayesian Learningcited by 3
  16. 2024Multi-Bit Mechanism: A Novel Information Transmission Paradigm for Spiking Neural Networkscited by 3
  17. 2024Xpikeformer: Hybrid Analog-Digital Hardware Acceleration for Spiking Transformerscited by 3
  18. 2024Direct Training Needs Regularisation: Anytime Optimal Inference Spiking Neural Networkcited by 2

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learningcited by 1
  2. 2025Neuromorphic Wireless Split Computing with Resonate-and-Fire Neuronscited by 0
  3. 2025Online Reliable Anomaly Detection via Neuromorphic Sensing and Communicationscited by 0