Summary of Sfc: Achieve Accurate Fast Convolution Under Low-precision Arithmetic, by Liulu He et al.
SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic
by Liulu He, Yufei Zhao, Rui Gao, Yuan Du, Li Du
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV); Signal Processing (eess.SP); Numerical Analysis (math.NA)
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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 algebraic transform called SFC, which extends Discrete Fourier Transform (DFT) with symbolic computing to accelerate quantized convolution operations in deep models. This approach eliminates the need for high-precision arithmetic and reduces multiplication requirements by 3.68x for 3×3 convolutions. The authors also introduce correction terms to convert invalid circular convolution outputs into effective ones, enabling accurate inference while maintaining efficiency. A numerical error analysis is presented, demonstrating the effectiveness of SFC in reducing computational complexity while preserving accuracy. Experimental results on benchmarks and FPGA demonstrate that SFC outperforms existing works on fast convolution quantization, offering a promising solution for efficient deep learning model deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds new ways to make computer calculations faster and more accurate. It introduces an algorithm called SFC that helps with a type of math problem called convolution. This is important because it can be used in artificial intelligence models like those used in self-driving cars or medical diagnosis tools. The authors show that their method is better than existing methods at reducing the amount of work needed to perform these calculations while still keeping the results accurate. They tested their algorithm on different types of computers and showed that it works well in real-world applications. |
Keywords
» Artificial intelligence » Deep learning » Inference » Precision » Quantization
