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.

翻译：脉冲神经网络（SNN）作为一种受大脑启发的策略，因其内在脉冲信息状态带来的高稀疏性与低功耗特性而备受关注。为进一步提升SNN效率，部分研究宣称人工神经网络（ANN）中存在不牺牲原始网络性能的子网络，即彩票假设（LTs）在SNN中同样成立。然而，SNN处理的脉冲信息在稀疏化过程中与二值化具有天然相似性与亲和性。因此，为进一步探索SNN效率，本文聚焦于：（1）二元SNN中是否存在彩票，（2）脉冲机制相比于简单模型二值化是否为处理二元信息的优越策略。为验证这些假设，我们提出一种稀疏训练方法，在不同网络结构下寻找二元权重脉冲彩票（BinW-SLT）。通过全面评估，我们表明与二元彩票相比，BinW-SLT在CIFAR-10和CIFAR-100上分别可实现高达+5.86%和+3.17%的性能提升，相较于全精度SNN和ANN可分别节省1.86倍和8.92倍能耗。