Summary of Improving Quantization-aware Training Of Low-precision Network Via Block Replacement on Full-precision Counterpart, by Chengting Yu et al.
Improving Quantization-aware Training of Low-Precision Network via Block Replacement on Full-Precision Counterpart
by Chengting Yu, Shu Yang, Fengzhao Zhang, Hanzhi Ma, Aili Wang, Er-Ping Li
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a general framework for quantization-aware training (QAT) to alleviate the limitations of direct low-precision network training. The framework permits full-precision guidance during both forward and backward processes, enabling the integration of quantized blocks into full-precision networks throughout training. This approach achieves state-of-the-art results for 4-, 3-, and 2-bit quantization on ImageNet and CIFAR-10. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a way to make computers do more tasks with less information. It’s like trying to draw a picture with only two colors instead of many, but still get the same result as if you used many colors. The new method helps computers work better even when they’re not using all their power. |
Keywords
» Artificial intelligence » Precision » Quantization
