The Vision Transformer (ViT) architecture has emerged as the backbone of choice for state-of-the-art deep models for computer vision applications. However, ViTs are ill-suited for private inference using secure multi-party computation (MPC) protocols, due to the large number of non-polynomial operations (self-attention, feed-forward rectifiers, layer normalization). We propose PriViT, a gradient based algorithm to selectively "Taylorize" nonlinearities in ViTs while maintaining their prediction accuracy. Our algorithm is conceptually simple, easy to implement, and achieves improved performance over existing approaches for designing MPC-friendly transformer architectures in terms of achieving the Pareto frontier in latency-accuracy. We confirm these improvements via experiments on several standard image classification tasks. Public code is available at https://github.com/NYU-DICE-Lab/privit.
@article{arxiv.2310.04604,
title = {PriViT: Vision Transformers for Fast Private Inference},
author = {Naren Dhyani and Jianqiao Mo and Minsu Cho and Ameya Joshi and Siddharth Garg and Brandon Reagen and Chinmay Hegde},
journal= {arXiv preprint arXiv:2310.04604},
year = {2023}
}