Spiking Neural Networks (SNNs), a novel brain-inspired algorithm, are garnering increased attention for their superior computation and energy efficiency over traditional artificial neural networks (ANNs). To facilitate deployment on memory-constrained devices, numerous studies have explored SNN pruning. However, these efforts are hindered by challenges such as scalability challenges in more complex architectures and accuracy degradation. Amidst these challenges, the Lottery Ticket Hypothesis (LTH) emerges as a promising pruning strategy. It posits that within dense neural networks, there exist winning tickets or subnetworks that are sparser but do not compromise performance. To explore a more structure-sparse and energy-saving model, we investigate the unique synergy of SNNs with LTH and design two novel spiking winning tickets to push the boundaries of sparsity within SNNs. Furthermore, we introduce an innovative algorithm capable of simultaneously identifying both weight and patch-level winning tickets, enabling the achievement of sparser structures without compromising on the final model's performance. Through comprehensive experiments on both RGB-based and event-based datasets, we demonstrate that our spiking lottery ticket achieves comparable or superior performance even when the model structure is extremely sparse.

翻译：脉冲神经网络（SNN）作为一种新型类脑算法，因其比传统人工神经网络（ANN）具有更优的计算效率与能量效率而备受关注。为便于部署在内存受限设备上，大量研究探索了SNN剪枝技术。然而，这些努力因更复杂架构中的可扩展性挑战和精度下降等问题而受阻。在此背景下，彩票假说（Lottery Ticket Hypothesis, LTH）作为一种有前景的剪枝策略脱颖而出。该假说认为，稠密神经网络中存在具有更稀疏结构但不影响性能的中奖子网络。为探索结构更稀疏且更节能的模型，我们研究了SNN与LTH的独特协同作用，设计了两种新型脉冲中奖子网络以推动SNN稀疏性边界。此外，我们提出一种创新算法，能够同时识别权重级与补丁级中奖子网络，从而在确保最终模型性能的同时实现更稀疏的结构。通过在基于RGB和基于事件的数据集上进行全面实验，我们证明即使模型结构极度稀疏，所提出的脉冲中奖子网络仍能达到相当或更优的性能。