量化神经网络时，精度之外还需关注放电行为是否保持一致。
Quantization of Spiking Neural Networks Beyond Accuracy
- 用地球移动距离衡量放电分布差异，比传统指标更敏感
- 同等精度下，不同量化方式导致放电模式显著不同
- 适合关注部署效率与稀疏性保持的SNN研究者
量化是脉冲神经网络（Spiking Neural Networks, SNN）在资源受限硬件上部署的重要手段，可降低内存带宽和计算成本。然而现有SNN量化评估几乎只关注精度，忽略了量化后网络是否保留了全精度模型的放电行为。我们发现，量化方法、裁剪范围和位宽在相同精度下会产生显著不同的放电分布，这些差异无法被标准指标捕捉，却直接影响实际部署中的有效稀疏性、状态存储和事件处理负载。为此，我们提出使用地球移动距离（Earth Mover's Distance, EMD）作为诊断指标，系统评估在CIFAR-10和CIFAR-100上训练的SEW-ResNet架构中权重与膜电位量化的情况。结果表明，均匀量化即使在精度不变时也会引发放电分布漂移，而类似LQ-Net的自学习量化能较好保持放电行为。研究建议将行为保真度作为与精度并列的评估标准，且EMD可作为评估该标准的合理工具。
原文摘要 · Abstract (English)
Quantization is a natural complement to the sparse, event-driven computation of Spiking Neural Networks, reducing memory bandwidth and arithmetic cost for deployment on resource-constrained hardware. However, existing SNN quantization evaluation focuses almost exclusively on accuracy, overlooking whether a quantized network preserves the firing behavior of its full-precision counterpart. We demonstrate that quantization method, clipping range, and bit-width can produce substantially different firing distributions at equivalent accuracy, differences invisible to standard metrics but relevant to deployment, where firing activity governs effective sparsity, state storage, and event-processing load. To capture this gap, we propose Earth Mover's Distance as a diagnostic metric for firing distribution divergence, and apply it systematically across weight and membrane quantization on SEW-ResNet architectures trained on CIFAR-10 and CIFAR-100. We find that uniform quantization induces distributional drift even when accuracy is preserved, while LQ-Net style learned quantization maintains firing behavior close to the full-precision baseline. Our results suggest that behavior preservation should be treated as an evaluation criterion alongside accuracy, and that EMD provides a principled tool for assessing it.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。