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Chip design with machine learning: a survey from algorithm perspective

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Abstract

Chip design with machine learning (ML) has been widely explored to achieve better designs, lower runtime costs, and no human-in-the-loop process. However, with tons of work, there is a lack of clear links between the ML algorithms and the target problems, causing a huge gap in understanding the potential and possibility of ML in future chip design. This paper comprehensively surveys existing studies in chip design with ML from an algorithm perspective. To achieve this goal, we first propose a novel and systematical taxonomy that divides target problems in chip design into three categories. Then, to solve the target problems with ML algorithms, we formulate the three categories as three ML problems correspondingly. Based on the taxonomy, we conduct a comprehensive survey in terms of target problems based on different ML algorithms. Finally, we conclude three key challenges for existing studies and highlight several insights for the future development of chip design with machine learning. By constructing a clear link between chip design problems and ML solutions, we hope the survey can shed light on the road to chip design intelligence from previous chip design automation.

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grant Nos. 61925208, 62222214, 62102399, U22A2028, U19B2019), Beijing Academy of Artificial Intelligence (BAAI), CAS Project for Young Scientists in Basic Research (Grant No. YSBR-029), and Youth Innovation Promotion Association CAS and Xplore Prize.

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  1. State Key Lab of Processor, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Wenkai He, Xiaqing Li, Xinkai Song, Yifan Hao, Rui Zhang, Zidong Du & Yunji Chen

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Wenkai He & Yunji Chen

  3. Cambricon Technologies, Beijing, 100191, China

    Wenkai He, Xinkai Song, Yifan Hao & Rui Zhang

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  1. Wenkai He
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  2. Xiaqing Li
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Correspondence to Yunji Chen.

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He, W., Li, X., Song, X. et al. Chip design with machine learning: a survey from algorithm perspective. Sci. China Inf. Sci. 66, 211101 (2023). https://doi.org/10.1007/s11432-022-3772-8

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