Summary of Hlq: Fast and Efficient Backpropagation Via Hadamard Low-rank Quantization, by Seonggon Kim et al.
HLQ: Fast and Efficient Backpropagation via Hadamard Low-rank Quantization
by Seonggon Kim, Eunhyeok Park
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposes a novel optimization strategy called Hadamard Low-rank Quantization (HLQ) to reduce the cost of backpropagation in convolutional and linear layers. The authors analyze the sensitivity of gradient computation and design a pipeline that applies 4-bit Hadamard quantization to activation gradients and Hadamard low-rank approximation to weight gradients. The combination is found to be optimal, achieving significant memory savings and acceleration on real GPUs with negligible quality degradation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make AI models faster and more efficient. Right now, it takes a lot of computer power to train these models, which can be a problem when you need to use them for many tasks at once. The researchers came up with a clever solution called Hadamard Low-rank Quantization (HLQ). They tested this method on some big datasets and found that it not only saved memory but also made the training process much faster. |
Keywords
» Artificial intelligence » Backpropagation » Optimization » Quantization
