Summary of Trio-vit: Post-training Quantization and Acceleration For Softmax-free Efficient Vision Transformer, by Huihong Shi et al.
Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision Transformer
by Huihong Shi, Haikuo Shao, Wendong Mao, Zhongfeng Wang
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a new approach to accelerate Vision Transformers (ViTs) for efficient deployment on embedded devices. Building upon the success of Transformers in natural language processing, ViTs have achieved remarkable performance in various computer vision tasks. However, their large model sizes and computations hinder their deployment, highlighting the need for effective model compression methods like quantization. The authors focus on developing a tailored post-training quantization engine to boost accuracy while integrating linear attention with low computational complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to make Vision Transformers smaller and faster so they can be used on devices that don’t have as much power or memory. This is important because big models like ViTs are really good at doing certain tasks, but they’re too slow and take up too much space to use everywhere. |
Keywords
» Artificial intelligence » Attention » Model compression » Natural language processing » Quantization
