  Vision Transformers (ViTs) have recently garnered considerable attention,
emerging as a promising alternative to convolutional neural networks (CNNs) in
several vision-related applications. However, their large model sizes and high
computational and memory demands hinder deployment, especially on
resource-constrained devices. This underscores the necessity of
algorithm-hardware co-design specific to ViTs, aiming to optimize their
performance by tailoring both the algorithmic structure and the underlying
hardware accelerator to each other's strengths. Model quantization, by
converting high-precision numbers to lower-precision, reduces the computational
demands and memory needs of ViTs, allowing the creation of hardware
specifically optimized for these quantized algorithms, boosting efficiency.
This article provides a comprehensive survey of ViTs quantization and its
hardware acceleration. We first delve into the unique architectural attributes
of ViTs and their runtime characteristics. Subsequently, we examine the
fundamental principles of model quantization, followed by a comparative
analysis of the state-of-the-art quantization techniques for ViTs.
Additionally, we explore the hardware acceleration of quantized ViTs,
highlighting the importance of hardware-friendly algorithm design. In
conclusion, this article will discuss ongoing challenges and future research
paths. We consistently maintain the related open-source materials at
https://github.com/DD-DuDa/awesome-vit-quantization-acceleration.
