Summary of Genetic Quantization-aware Approximation For Non-linear Operations in Transformers, by Pingcheng Dong et al.
Genetic Quantization-Aware Approximation for Non-Linear Operations in Transformers
by Pingcheng Dong, Yonghao Tan, Dong Zhang, Tianwei Ni, Xuejiao Liu, Yu Liu, Peng Luo, Luhong Liang, Shih-Yang Liu, Xijie Huang, Huaiyu Zhu, Yun Pan, Fengwei An, Kwang-Ting Cheng
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR); Neural and Evolutionary Computing (cs.NE)
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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 novel algorithm, Genetic LUT-Approximation (GQA-LUT), to optimize non-linear functions in Transformers and their variants. This is achieved by automatically determining parameters for look-up tables (LUT) with quantization awareness. The results show that GQA-LUT achieves negligible degradation on semantic segmentation tasks for both vanilla and linear Transformer models. Additionally, the algorithm enables INT8-based LUT-Approximation, which provides significant area savings (81.3-81.7%) and power reduction (79.3-80.2%) compared to high-precision FP/INT 32 alternatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a way to make machine learning models run faster and use less energy on computers. It creates an algorithm called GQA-LUT that helps reduce the need for precise calculations in certain types of computer chips. This makes it possible to build smaller, more efficient devices that can do things like recognize pictures or understand speech. The results show that this approach works well and could be used in real-world applications. |
Keywords
» Artificial intelligence » Machine learning » Precision » Quantization » Semantic segmentation » Transformer
