Summary of 1-bit Fqt: Pushing the Limit Of Fully Quantized Training to 1-bit, by Chang Gao et al.
1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit
by Chang Gao, Jianfei Chen, Kang Zhao, Jiaqi Wang, Liping Jing
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 proposed Fully Quantized Training (FQT) accelerates deep neural network training by reducing activations, weights, and gradients to lower precision. The paper explores the limits of FQT by attempting 1-bit quantization and providing theoretical analysis based on Adam and SGD. The authors introduce Activation Gradient Pruning (AGP), which prunes less informative gradients and enhances remaining gradients’ numerical precision to mitigate gradient variance. They also propose Sample Channel joint Quantization (SCQ) for low-bitwidth hardware-friendly training. The framework is deployed for fine-tuning VGGNet-16 and ResNet-18 on multiple datasets, achieving an average accuracy improvement of approximately 6% compared to per-sample quantization and a maximum speedup of 5.13x compared to full precision training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to make deep learning models work faster. It’s like a superpower for computers! They tried to reduce the amount of information needed to train these models, making them run much quicker. The scientists developed two new techniques: one that gets rid of less important information and another that helps the model work better with lower precision. This means it can run on older or cheaper computers that aren’t as powerful. The results show that this new way of training makes models about 6% more accurate and up to 5 times faster than before. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Neural network » Precision » Pruning » Quantization » Resnet
