Summary of Memory-efficient 4-bit Preconditioned Stochastic Optimization, by Jingyang Li et al.
Memory-Efficient 4-bit Preconditioned Stochastic Optimization
by Jingyang Li, Kuangyu Ding, Kim-Chuan Toh, Pan Zhou
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Optimization and Control (math.OC)
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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 introduces a novel approach to preconditioned stochastic optimization algorithms, specifically for large-scale neural network training. By applying 4-bit quantization to the preconditioners, researchers can reduce memory usage while preserving spectral properties. Two key methods are proposed: Cholesky decomposition followed by quantization of the factors, and error feedback in the quantization process. Experimental results show that this approach enhances memory efficiency and algorithm performance in deep-learning tasks. Theoretical convergence proofs are also provided for both smooth and non-smooth stochastic optimization settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to train large neural networks faster and using less memory. To do this, they came up with a way to shrink the size of the numbers used in the calculations without losing important information. This is done by using a special type of math called Cholesky decomposition, which helps reduce the amount of memory needed. They also added an “error feedback” system that makes sure the calculations are still accurate. The results show that this new approach works well and could be useful for training big AI models. |
Keywords
» Artificial intelligence » Deep learning » Neural network » Optimization » Quantization
