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With the increasing popularity and the increasing size of vision transformers(ViTs), there has been an increasing interest in making them more efficient andless computationally costly for deployment on edge devices with limitedcomputing resources. Binarization can be used to help reduce the size of ViTmodels and their computational cost significantly, using popcount operationswhen the weights and the activations are in binary. However, ViTs suffer alarger performance drop when directly applying convolutional neural network(CNN) binarization methods or existing binarization methods to binarize ViTscompared to CNNs on datasets with a large number of classes such asImageNet-1k. With extensive analysis, we find that binary vanilla ViTs such asDeiT miss out on a lot of key architectural properties that CNNs have thatallow binary CNNs to have much higher representational capability than binaryvanilla ViT. Therefore, we propose BinaryViT, in which inspired by the CNNarchitecture, we include operations from the CNN architecture into a pure ViTarchitecture to enrich the representational capability of a binary ViT withoutintroducing convolutions. These include an average pooling layer instead of atoken pooling layer, a block that contains multiple average pooling branches,an affine transformation right before the addition of each main residualconnection, and a pyramid structure. Experimental results on the ImageNet-1kdataset show the effectiveness of these operations that allow a binary pure ViTmodel to be competitive with previous state-of-the-art (SOTA) binary CNNmodels.

BinaryViT: Pushing Binary Vision Transformers Towards Convolutional Models