  Recent years have witnessed the great success of vision transformer (ViT),
which has achieved state-of-the-art performance on multiple computer vision
benchmarks. However, ViT models suffer from vast amounts of parameters and high
computation cost, leading to difficult deployment on resource-constrained edge
devices. Existing solutions mostly compress ViT models to a compact model but
still cannot achieve real-time inference. To tackle this issue, we propose to
explore the divisibility of transformer structure, and decompose the large ViT
into multiple small models for collaborative inference at edge devices. Our
objective is to achieve fast and energy-efficient collaborative inference while
maintaining comparable accuracy compared with large ViTs. To this end, we first
propose a collaborative inference framework termed DeViT to facilitate edge
deployment by decomposing large ViTs. Subsequently, we design a
decomposition-and-ensemble algorithm based on knowledge distillation, termed
DEKD, to fuse multiple small decomposed models while dramatically reducing
communication overheads, and handle heterogeneous models by developing a
feature matching module to promote the imitations of decomposed models from the
large ViT. Extensive experiments for three representative ViT backbones on four
widely-used datasets demonstrate our method achieves efficient collaborative
inference for ViTs and outperforms existing lightweight ViTs, striking a good
trade-off between efficiency and accuracy. For example, our DeViTs improves
end-to-end latency by 2.89$\times$ with only 1.65% accuracy sacrifice using
CIFAR-100 compared to the large ViT, ViT-L/16, on the GPU server. DeDeiTs
surpasses the recent efficient ViT, MobileViT-S, by 3.54% in accuracy on
ImageNet-1K, while running 1.72$\times$ faster and requiring 55.28% lower
energy consumption on the edge device.
