Summary of High Performance Im2win and Direct Convolutions Using Three Tensor Layouts on Simd Architectures, by Xiang Fu et al.
High Performance Im2win and Direct Convolutions using Three Tensor Layouts on SIMD Architectures
by Xiang Fu, Xinpeng Zhang, Jixiang Ma, Peng Zhao, Shuai Lu, Xu T. Liu
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper investigates the impact of tensor data layouts on convolution operations in deep neural networks, particularly in SIMD architectures. The authors develop three novel data layouts for im2win convolution (NHWC, CHWN, and CHWN8) and introduce optimization techniques for both direct and im2win convolutions. Experimental results show that optimized im2win convolution with the NHWC layout achieves up to 355% performance speedup compared to NCHW. The optimizations also improve the performance of direct and im2win convolutions, reaching up to 95% and 94% of machine’s theoretical peak performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how data layouts affect convolution operations in deep learning models. It creates new ways to store data for a specific type of convolution (im2win) and shows how this can make the calculations faster. The results show that one way of storing data (NHWC) is much faster than others, with a speedup of up to 355%. This could help make deep learning models work more efficiently. |
Keywords
» Artificial intelligence » Deep learning » Optimization
