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.

翻译：脉冲神经网络因其类脑的信息处理机制，引起了众多领域研究者的广泛关注。代理梯度的提出使得脉冲神经网络能够迁移到更复杂的任务中，并逐步缩小了与传统人工神经网络的差距。当前脉冲神经网络利用所有时刻的输出生成最终预测，这损害了其时域特性，导致性能与效率下降。我们提出了一种时域知识共享方法（TKS），该方法通过选择特定时刻的输出组成教师信号，结合真实标签指导网络训练，从而实现不同时刻之间的信息交互。我们在静态数据集CIFAR10、CIFAR100、ImageNet-1k以及神经形态数据集DVS-CIFAR10、NCALTECH101上验证了TKS。实验结果表明，与其他算法相比，我们取得了当前最优性能。在细粒度分类数据集（CUB-200-2011、StanfordDogs、StanfordCars）上的实验进一步证明了我们算法的优越性。TKS算法帮助模型具备更强的时域泛化能力，使网络在训练阶段可采用大时间步长保证性能，在测试阶段则可采用小时间步长。这极大便利了脉冲神经网络在边缘设备上的部署。