use a small decision network (binary classification task) inside the Transformer block to decide how to reduce the number of patches, self-attention heads and Transformer blocks;

obtained more than 2× improvement in efficiency compared to state-of-the-art vision transformers with only 0.8% drop of accuracy on ImageNet