Spiking neural networks (SNNs) have gained attention as a promising alternative to traditional artificial neural networks (ANNs) due to their potential for energy efficiency and their ability to model spiking behavior in biological systems. However, the training of SNNs is still a challenging problem, and new techniques are needed to improve their performance. In this paper, we study the impact of skip connections on SNNs and propose a hyperparameter optimization technique that adapts models from ANN to SNN. We demonstrate that optimizing the position, type, and number of skip connections can significantly improve the accuracy and efficiency of SNNs by enabling faster convergence and increasing information flow through the network. Our results show an average +8% accuracy increase on CIFAR-10-DVS and DVS128 Gesture datasets adaptation of multiple state-of-the-art models.

翻译：脉冲神经网络（SNN）因具备能效优势及模拟生物系统脉冲行为的能力，作为传统人工神经网络（ANN）的潜在替代方案而备受关注。然而，SNN的训练仍是具有挑战性的问题，亟需新技术提升其性能。本文系统研究了跳跃连接对SNN的影响，并提出一种将ANN模型迁移至SNN的超参数优化技术。我们证明，通过优化跳跃连接的位置、类型和数量，能够加速网络收敛并增强信息流，从而显著提升SNN的准确率与效率。实验结果显示，在CIFAR-10-DVS和DVS128 Gesture数据集上对多种先进模型进行适应性改造后，平均准确率提升了8%。