Summary of Pqv-mobile: a Combined Pruning and Quantization Toolkit to Optimize Vision Transformers For Mobile Applications, by Kshitij Bhardwaj
PQV-Mobile: A Combined Pruning and Quantization Toolkit to Optimize Vision Transformers for Mobile Applications
by Kshitij Bhardwaj
First submitted to arxiv on: 15 Aug 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 This paper presents a combined pruning and quantization tool called PQV-Mobile to optimize Vision Transformers (ViTs) for mobile applications. While ViTs are effective at computer vision tasks, they are complex and memory-intensive, making them unsuitable for resource-constrained mobile/edge systems. The tool supports different types of structured pruning based on magnitude importance, Taylor importance, and Hessian importance, as well as quantization from FP32 to FP16 and int8 targeting various mobile hardware backends. Experiments demonstrate the capabilities of PQV-Mobile, showcasing latency-memory-accuracy trade-offs for different amounts of pruning and int8 quantization using Facebook’s Data Efficient Image Transformer (DeiT) models. The results show that even with a 9.375% pruning rate and int8 quantization from FP32, followed by optimization for mobile applications, there is a significant latency reduction of 7.18X with only a small accuracy loss of 2.24%. PQV-Mobile is an open-source tool. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making special computer models called Vision Transformers work better on phones and other devices that don’t have a lot of power or memory. These models are really good at recognizing images, but they take up too much space and use too many resources. The researchers created a new tool to help make these models smaller and faster, so they can be used on mobile devices without sacrificing too much performance. They tested their tool with some popular image recognition models and showed that it can make them run 7 times faster while only losing a little bit of accuracy. This tool is available for anyone to use. |
Keywords
» Artificial intelligence » Optimization » Pruning » Quantization » Transformer
