  Spiking neural networks (SNNs) are emerging as an energy-efficient
alternative to traditional artificial neural networks (ANNs) due to their
unique spike-based event-driven nature. Coding is crucial in SNNs as it
converts external input stimuli into spatio-temporal feature sequences.
However, most existing deep SNNs rely on direct coding that generates powerless
spike representation and lacks the temporal dynamics inherent in human vision.
Hence, we introduce Gated Attention Coding (GAC), a plug-and-play module that
leverages the multi-dimensional gated attention unit to efficiently encode
inputs into powerful representations before feeding them into the SNN
architecture. GAC functions as a preprocessing layer that does not disrupt the
spike-driven nature of the SNN, making it amenable to efficient neuromorphic
hardware implementation with minimal modifications. Through an observer model
theoretical analysis, we demonstrate GAC's attention mechanism improves
temporal dynamics and coding efficiency. Experiments on CIFAR10/100 and
ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with
remarkable efficiency. Notably, we improve top-1 accuracy by 3.10\% on CIFAR100
with only 6-time steps and 1.07\% on ImageNet while reducing energy usage to
66.9\% of the previous works. To our best knowledge, it is the first time to
explore the attention-based dynamic coding scheme in deep SNNs, with
exceptional effectiveness and efficiency on large-scale datasets.The Code is
available at https://github.com/bollossom/GAC.
