Summary of Model Quantization and Hardware Acceleration For Vision Transformers: a Comprehensive Survey, by Dayou Du et al.
Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey
by Dayou Du, Gu Gong, Xiaowen Chu
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Computer Vision and Pattern Recognition (cs.CV); Performance (cs.PF)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper discusses the limitations of Vision Transformers (ViTs) in deployment on resource-constrained devices due to their large model sizes and high computational demands. To optimize ViT performance, algorithm-hardware co-design is necessary, focusing on quantization techniques that reduce precision numbers while maintaining accuracy. The authors provide a comprehensive survey of ViT quantization and hardware acceleration, exploring architectural attributes, runtime characteristics, model quantization principles, state-of-the-art quantization methods, and hardware-friendly design considerations. Key contributions include a comparative analysis of quantization techniques and the exploration of hardware acceleration for quantized ViTs. This work highlights the importance of algorithm-hardware co-design for efficient deployment of ViTs on resource-constrained devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making a type of computer vision technology called Vision Transformers (ViTs) work better on devices with limited resources, like smartphones or old computers. The problem is that these devices can’t handle the large size and high demands of ViTs. To solve this issue, researchers are working on “quantizing” ViTs, which means making them smaller and more efficient without losing their accuracy. The paper explains how to do this and why it’s important for getting ViTs to work on resource-constrained devices. |
Keywords
» Artificial intelligence » Precision » Quantization » Vit
