Summary of Differentiable Search For Finding Optimal Quantization Strategy, by Lianqiang Li et al.
Differentiable Search for Finding Optimal Quantization Strategy
by Lianqiang Li, Chenqian Yan, Yefei Chen
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV)
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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 A novel differentiable quantization strategy search (DQSS) is proposed to optimize quantization for individual layers in deep neural networks. Existing algorithms uniformly apply a quantization strategy across all layers, neglecting layer-specific characteristics. DQSS formulates this problem as a differentiable neural architecture search and employs an efficient convolution to explore mixed quantization strategies through gradient-based optimization. The approach enables post-training quantization and outperforms state-of-the-art methods in image classification and super-resolution tasks with various network architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning researchers have been trying to speed up and shrink big computer models called deep neural networks (DNNs). To do this, they use special tricks to make the models smaller. But these tricks don’t always work best for every part of the model. A new way is being developed that looks at each part of the model separately and finds the best trick to use just for that part. This helps the model be even faster and better than before. The new method was tested on two big tasks: recognizing objects in pictures and making blurry pictures clearer. It worked really well and could help make computer models even more powerful. |
Keywords
* Artificial intelligence * Deep learning * Image classification * Optimization * Quantization * Super resolution
