  Recently, brain-inspired spiking neuron networks (SNNs) have attracted
widespread research interest because of their event-driven and energy-efficient
characteristics. Still, it is difficult to efficiently train deep SNNs due to
the non-differentiability of its activation function, which disables the
typically used gradient descent approaches for traditional artificial neural
networks (ANNs). Although the adoption of surrogate gradient (SG) formally
allows for the back-propagation of losses, the discrete spiking mechanism
actually differentiates the loss landscape of SNNs from that of ANNs, failing
the surrogate gradient methods to achieve comparable accuracy as for ANNs. In
this paper, we first analyze why the current direct training approach with
surrogate gradient results in SNNs with poor generalizability. Then we
introduce the temporal efficient training (TET) approach to compensate for the
loss of momentum in the gradient descent with SG so that the training process
can converge into flatter minima with better generalizability. Meanwhile, we
demonstrate that TET improves the temporal scalability of SNN and induces a
temporal inheritable training for acceleration. Our method consistently
outperforms the SOTA on all reported mainstream datasets, including
CIFAR-10/100 and ImageNet. Remarkably on DVS-CIFAR10, we obtained 83$\%$ top-1
accuracy, over 10$\%$ improvement compared to existing state of the art. Codes
are available at \url{https://github.com/Gus-Lab/temporal_efficient_training}.
