Optimal Spiking Brain Compression: Improving One-Shot Post-Training Pruning and Quantization for Spiking Neural Networks

• URL: http://arxiv.org/abs/2506.03996v1
• Date: Wed, 04 Jun 2025 14:23:05 GMT
• Title: Optimal Spiking Brain Compression: Improving One-Shot Post-Training Pruning and Quantization for Spiking Neural Networks
• Authors: Lianfeng Shi, Ao Li, Benjamin Ward-Cherrier,
• Abstract summary: Spiking Neural Networks (SNNs) have emerged as a new generation of energy-efficient neural networks suitable for implementation on neuromorphic hardware.<n>Weight pruning and quantization have recently been explored to improve SNNs' efficiency.<n>We propose a new one-shot post-training pruning/quantization framework, Optimal Spiking Brain Compression (OSBC) for SNNs.
• Score: 2.3222699639842244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking Neural Networks (SNNs) have emerged as a new generation of energy-efficient neural networks suitable for implementation on neuromorphic hardware. As neuromorphic hardware has limited memory and computing resources, weight pruning and quantization have recently been explored to improve SNNs' efficiency. State-of-the-art SNN pruning/quantization methods employ multiple compression and training iterations, increasing the cost for pre-trained or very large SNNs. In this paper, we propose a new one-shot post-training pruning/quantization framework, Optimal Spiking Brain Compression (OSBC), that adapts the Optimal Brain Compression (OBC) method of [Frantar, Singh, and Alistarh, 2023] for SNNs. Rather than minimizing the loss on neuron input current as OBC does, OSBC achieves more efficient and accurate SNN compression in one pass by minimizing the loss on spiking neuron membrane potential with a small sample dataset. Our experiments on neuromorphic datasets (N-MNIST, CIFAR10-DVS, DVS128-Gesture) demonstrate that OSBC can achieve 97% sparsity through pruning with 1.41%, 10.20%, and 1.74% accuracy loss, or 4-bit symmetric quantization with 0.17%, 1.54%, and 7.71% accuracy loss, respectively. Code will be available on GitHub.

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