Spiking Neural Network (SNN) as a brain-inspired strategy receives lots of
attention because of the high-sparsity and low-power properties derived from
its inherent spiking information state. To further improve the efficiency of
SNN, some works declare that the Lottery Tickets (LTs) Hypothesis, which
indicates that the Artificial Neural Network (ANN) contains a subnetwork
without sacrificing the performance of the original network, also exists in
SNN. However, the spiking information handled by SNN has a natural similarity
and affinity with binarization in sparsification. Therefore, to further explore
SNN efficiency, this paper focuses on (1) the presence or absence of LTs in the
binary SNN, and (2) whether the spiking mechanism is a superior strategy in
terms of handling binary information compared to simple model binarization. To
certify these consumptions, a sparse training method is proposed to find Binary
Weights Spiking Lottery Tickets (BinW-SLT) under different network structures.
Through comprehensive evaluations, we show that BinW-SLT could attain up to
+5.86% and +3.17% improvement on CIFAR-10 and CIFAR-100 compared with binary
LTs, as well as achieve 1.86x and 8.92x energy saving compared with
full-precision SNN and ANN.

通过使用稀疏训练方法，本文研究了在不同网络结构下找到二进制权重尖峰票（BinW-SLT）是否存在于二进制 SNN 中，以及与简单模型二值化相比，尖峰机制是否在处理二进制信息方面是一种更有效的策略。通过全面评估，我们发现 BinW-SLT 在 CIFAR-10 和 CIFAR-100 上相比于二进制 LTs 提升了 + 5.86% 和 + 3.17%，并且与全精度的 SNN 和 ANN 相比，实现了 1.86 倍和 8.92 倍的能源节省。