Abstract
Spiking Neural Networks (SNNs) are increasingly studied as energy-efficient alternatives to Convolutional Neural Networks (CNNs), particularly for edge intelligence. However, prior work has largely emphasized large-scale models, leaving the design and evaluation of lightweight CNN-to-SNN pipelines underexplored. In this paper, we present the first systematic benchmark of lightweight SNNs obtained by converting compact CNN architectures into spiking networks, where activations are modeled with Leaky-Integrate-and-Fire (LIF) neurons and trained using surrogate gradient descent under a unified setup. We construct spiking variants of ShuffleNet, SqueezeNet, MnasNet, and MixNet, and evaluate them on CIFAR-10, CIFAR-100, and TinyImageNet, measuring accuracy, F1-score, parameter count, computational complexity, and energy consumption. Our results show that SNNs can achieve up to 15.7x higher energy efficiency than their CNN counterparts while retaining competitive accuracy. Among these, the SNN variant of SqueezeNet consistently outperforms other lightweight SNNs. To further optimize this model, we apply a structured pruning strategy that removes entire redundant modules, yielding a pruned architecture, SNN-SqueezeNet-P. This pruned model improves CIFAR-10 accuracy by 6% and reduces parameters by 19% compared to the original SNN-SqueezeNet. Crucially, it narrows the gap with CNN-SqueezeNet, achieving nearly the same accuracy (only 1% lower) but with an 88.1% reduction in energy consumption due to sparse spike-driven computations. Together, these findings establish lightweight SNNs as practical, low-power alternatives for edge deployment, highlighting a viable path toward deploying high-performance, low-power intelligence on the edge.
Abstract (translated)
脉冲神经网络（SNN）作为卷积神经网络（CNN）的节能替代方案，特别是在边缘智能领域中得到了越来越多的关注。然而，先前的研究主要集中在大规模模型上，忽略了轻量级 CNN 到 SNN 转换管道的设计和评估。在这篇论文中，我们介绍了首次系统性地对通过将紧凑型 CNN 架构转换为脉冲网络获得的轻量级 SNN 进行基准测试的结果。在这些转换后的 SNN 中，激活被建模为使用泄露积分放电（LIF）神经元，并采用统一设置下的替代梯度下降法进行训练。 我们构建了 ShuffleNet、SqueezeNet、MnasNet 和 MixNet 的脉冲版本，并针对 CIFAR-10、CIFAR-100 和 TinyImageNet 数据集进行了评估，包括准确率、F1 分数、参数数量、计算复杂性以及能耗等指标。我们的结果显示，在保持竞争力的准确性的前提下，SNN 可以比其 CNN 对应模型高 15.7 倍的能量效率。在这其中，SqueezeNet 的 SNN 变体在所有轻量级 SNN 中表现最优。 为了进一步优化这个模型，我们应用了一种结构化剪枝策略，删除了整个冗余模块，从而得到了一个剪枝架构：SNN-SqueezeNet-P。相比原始的 SNN-SqueezeNet，此剪枝模型将 CIFAR-10 准确率提高了 6%，参数减少了 19%。尤为重要的是，它缩小了与 CNN-SqueezeNet 的差距，在几乎相同的准确度（低仅 1%）下，由于稀疏脉冲驱动的计算，能量消耗降低了 88.1%。 这些发现共同证明轻量级 SNN 在边缘部署中是实用且低能耗的选择，并为在边缘部署高性能、低功耗智能提供了一条可行路径。
URL
https://arxiv.org/abs/2602.09717
