  Deep spiking neural networks (SNNs) have drawn much attention in recent years
because of their low power consumption, biological rationality and event-driven
property. However, state-of-the-art deep SNNs (including Spikformer and
Spikingformer) suffer from a critical challenge related to the imprecise
gradient backpropagation. This problem arises from the improper design of
downsampling modules in these networks, and greatly hampering the overall model
performance. In this paper, we propose ConvBN-MaxPooling-LIF (CML), an
SNN-optimized downsampling with precise gradient backpropagation. We prove that
CML can effectively overcome the imprecision of gradient backpropagation from a
theoretical perspective. In addition, we evaluate CML on ImageNet, CIFAR10,
CIFAR100, CIFAR10-DVS, DVS128-Gesture datasets, and show state-of-the-art
performance on all these datasets with significantly enhanced performances
compared with Spikingformer. For instance, our model achieves 77.64 $\%$ on
ImageNet, 96.04 $\%$ on CIFAR10, 81.4$\%$ on CIFAR10-DVS, with + 1.79$\%$ on
ImageNet, +1.16$\%$ on CIFAR100 compared with Spikingformer.
