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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.01223 |
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| _version_ | 1866913972018479104 |
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| author | Xu, Changqing Sun, Guoqing Liu, Yi Liao, Xinfang Yang, Yintang |
| author_facet | Xu, Changqing Sun, Guoqing Liu, Yi Liao, Xinfang Yang, Yintang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01223 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ParaRevSNN: A Parallel Reversible Spiking Neural Network for Efficient Training and Inference Xu, Changqing Sun, Guoqing Liu, Yi Liao, Xinfang Yang, Yintang Computer Vision and Pattern Recognition 68T10 I.4.6 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. |
| title | ParaRevSNN: A Parallel Reversible Spiking Neural Network for Efficient Training and Inference |
| topic | Computer Vision and Pattern Recognition 68T10 I.4.6 |
| url | https://arxiv.org/abs/2508.01223 |