Spiking neural networks have attracted extensive attention from researchers
in many fields due to their brain-like information processing mechanism. The
proposal of surrogate gradient enables the spiking neural networks to migrate
to more complex tasks, and gradually close the gap with the conventional
artificial neural networks. Current spiking neural networks utilize the output
of all moments to produce the final prediction, which compromises their
temporal characteristics and causes a reduction in performance and efficiency.
We propose a temporal knowledge sharing approach (TKS) that enables the
interaction of information between different moments, by selecting the output
of specific moments to compose teacher signals to guide the training of the
network along with the real labels. We have validated TKS on both static
datasets CIFAR10, CIFAR100, ImageNet-1k and neuromorphic datasets DVS-CIFAR10,
NCALTECH101. Our experimental results indicate that we have achieved the
current optimal performance in comparison with other algorithms. Experiments on
Fine-grained classification datasets further demonstrate our algorithm's
superiority with CUB-200-2011, StanfordDogs, and StanfordCars. TKS algorithm
helps the model to have stronger temporal generalization capability, allowing
the network to guarantee performance with large time steps in the training
phase and with small time steps in the testing phase. This greatly facilitates
the deployment of SNNs on edge devices.

提出一种叫做时间知识共享（TKS）的方法，它能够选择特定时刻的输出来构成教师信号以辅助神经网络的训练，从而显著提高了在静态和神经形态数据集上的分类表现，加强了网络的时间推广能力，使得其可以轻松地在边缘设备上部署。