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.

翻译：脉冲神经网络（SNNs）作为卷积神经网络（CNNs）的高效能替代方案日益受到关注，尤其在边缘智能领域。然而，先前的研究主要集中于大规模模型，对轻量级CNN到SNN转换流程的设计与评估尚不充分。本文首次系统性地对轻量级SNN进行了基准测试，这些网络通过将紧凑型CNN架构转换为脉冲网络获得，其中激活采用Leaky-Integrate-and-Fire（LIF）神经元建模，并在统一设置下使用代理梯度下降法进行训练。我们构建了ShuffleNet、SqueezeNet、MnasNet和MixNet的脉冲变体，并在CIFAR-10、CIFAR-100和TinyImageNet数据集上评估了它们的精度、F1分数、参数量、计算复杂度和能耗。实验结果表明，SNN在保持竞争力精度的同时，能效最高可比对应CNN提升15.7倍。其中，SqueezeNet的SNN变体在轻量级SNN中表现持续领先。为进一步优化该模型，我们采用结构化剪枝策略移除整个冗余模块，得到剪枝架构SNN-SqueezeNet-P。与原始SNN-SqueezeNet相比，该剪枝模型在CIFAR-10上的精度提升6%，参数量减少19%。关键的是，它显著缩小了与CNN-SqueezeNet的性能差距：在精度仅低1%的情况下，凭借脉冲驱动的稀疏计算实现了88.1%的能耗降低。这些发现共同确立了轻量级SNN作为边缘部署实用低功耗替代方案的可行性，为在边缘设备部署高性能、低功耗智能系统指明了可行路径。