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ParaRevSNN: A Parallel Reversible Spiking Neural Network for Efficient Training and Inference

arXiv 2508.01223 ↗First posted 2 Aug 2025cs.CVcited by 0

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Abstract (arXiv)

Reversible Spiking Neural Networks (RevSNNs) enable memory-efficient training by reconstructing forward activations during backpropagation, but suffer from high latency due to strictly sequential computation. To overcome this limitation, we propose ParaRevSNN, a parallel reversible SNN architecture that decouples sequential dependencies between reversible blocks while preserving reversibility. This design enables inter-block parallelism, significantly accelerating training and inference while retaining the memory-saving benefits of reversibility. Experiments on CIFAR10, CIFAR100, CIFAR10-DVS, and DVS128 Gesture demonstrate that ParaRevSNN matches or exceeds the accuracy of standard RevSNNs, while reducing training time by up to 35.2% and inference time to 18.15%, making it well-suited for deployment in resource-constrained scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 2 Aug 2025.

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18 of 33 references.

References · 18

  1. 2014NICE: Non-linear Independent Components Estimationcited by 1,058
  2. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 406
  3. 2021Deep Residual Learning in Spiking Neural Networkscited by 331
  4. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 278
  5. 2017The Reversible Residual Network: Backpropagation Without Storing Activationscited by 217
  6. 2016Training Deep Spiking Neural Networks using Backpropagationcited by 205
  7. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 198
  8. 2022Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representationcited by 89
  9. 2023SEENN: Towards Temporal Spiking Early-Exit Neural Networkscited by 41
  10. 2021Momentum Residual Neural Networkscited by 37
  11. 2019Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networkscited by 32
  12. 2019A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentationcited by 16
6 more
  1. 2023Unleashing the Potential of Spiking Neural Networks by Dynamic Confidencecited by 15
  2. 2024High-Performance Temporal Reversible Spiking Neural Networks with O(L) Training Memory and O(1) Inference Costcited by 14
  3. 2023A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perceptioncited by 13
  4. 2025Efficient Parallel Training Methods for Spiking Neural Networks with Constant Time Complexitycited by 5
  5. 2022Ultra-low Latency Spiking Neural Networks with Spatio-Temporal Compression and Synaptic Convolutional Blockcited by 4
  6. 2024Dr2Net: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuningcited by 3

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