Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks, which is suitable to be implemented on
low-power mobile/edge devices. As such devices have limited memory storage,
neural pruning on SNNs has been widely explored in recent years. Most existing
SNN pruning works focus on shallow SNNs (2~6 layers), however, deeper SNNs (>16
layers) are proposed by state-of-the-art SNN works, which is difficult to be
compatible with the current SNN pruning work. To scale up a pruning technique
towards deep SNNs, we investigate Lottery Ticket Hypothesis (LTH) which states
that dense networks contain smaller subnetworks (i.e., winning tickets) that
achieve comparable performance to the dense networks. Our studies on LTH reveal
that the winning tickets consistently exist in deep SNNs across various
datasets and architectures, providing up to 97% sparsity without huge
performance degradation. However, the iterative searching process of LTH brings
a huge training computational cost when combined with the multiple timesteps of
SNNs. To alleviate such heavy searching cost, we propose Early-Time (ET) ticket
where we find the important weight connectivity from a smaller number of
timesteps. The proposed ET ticket can be seamlessly combined with a common
pruning techniques for finding winning tickets, such as Iterative Magnitude
Pruning (IMP) and Early-Bird (EB) tickets. Our experiment results show that the
proposed ET ticket reduces search time by up to 38% compared to IMP or EB
methods. Code is available at Github.

该研究使用彩票票据原理，提出了一种适用于深度脉冲神经网络（SNN）的神经剪枝技术，即运用早期时间（ET）票据来减少彩票票据原理的搜索时间，该方法在不降低性能的情况下使得 SNN 的鲁棒性得到了极大提高。