  Vision Transformers (ViTs) have demonstrated remarkable performance in
various computer vision tasks. However, the high computational complexity
hinders ViTs' applicability on devices with limited memory and computing
resources. Although certain investigations have delved into the fusion of
convolutional layers with self-attention mechanisms to enhance the efficiency
of ViTs, there remains a knowledge gap in constructing tiny yet effective ViTs
solely based on the self-attention mechanism. Furthermore, the straightforward
strategy of reducing the feature channels in a large but outperforming ViT
often results in significant performance degradation despite improved
efficiency. To address these challenges, we propose a novel channel shuffle
module to improve tiny-size ViTs, showing the potential of pure self-attention
models in environments with constrained computing resources. Inspired by the
channel shuffle design in ShuffleNetV2 \cite{ma2018shufflenet}, our module
expands the feature channels of a tiny ViT and partitions the channels into two
groups: the \textit{Attended} and \textit{Idle} groups. Self-attention
computations are exclusively employed on the designated \textit{Attended}
group, followed by a channel shuffle operation that facilitates information
exchange between the two groups. By incorporating our module into a tiny ViT,
we can achieve superior performance while maintaining a comparable
computational complexity to the vanilla model. Specifically, our proposed
channel shuffle module consistently improves the top-1 accuracy on the
ImageNet-1K dataset for various tiny ViT models by up to 2.8\%, with the
changes in model complexity being less than 0.03 GMACs.
