Machine Learning for Electronic Design Automation: A Survey

Machine Learning for Electronic Design Automation: A Survey. Huang, G., Hu, J., He, Y., Liu, J., Ma, M., Shen, Z., Wu, J., Xu, Y., Zhang, H., Zhong, K., Ning, X., Ma, Y., Yang, H., Yu, B., Yang, H., & Wang, Y. ACM Transactions on Design Automation of Electronic Systems, 26(5):40:1–40:46, June, 2021.

Paper doi abstract bibtex

With the down-scaling of CMOS technology, the design complexity of very large-scale integrated is increasing. Although the application of machine learning (ML) techniques in electronic design automation (EDA) can trace its history back to the 1990s, the recent breakthrough of ML and the increasing complexity of EDA tasks have aroused more interest in incorporating ML to solve EDA tasks. In this article, we present a comprehensive review of existing ML for EDA studies, organized following the EDA hierarchy.

@article{huang_machine_2021,
	title = {Machine {Learning} for {Electronic} {Design} {Automation}: {A} {Survey}},
	volume = {26},
	issn = {1084-4309},
	shorttitle = {Machine {Learning} for {Electronic} {Design} {Automation}},
	url = {http://doi.org/10.1145/3451179},
	doi = {10.1145/3451179},
	abstract = {With the down-scaling of CMOS technology, the design complexity of very large-scale integrated is increasing. Although the application of machine learning (ML) techniques in electronic design automation (EDA) can trace its history back to the 1990s, the recent breakthrough of ML and the increasing complexity of EDA tasks have aroused more interest in incorporating ML to solve EDA tasks. In this article, we present a comprehensive review of existing ML for EDA studies, organized following the EDA hierarchy.},
	number = {5},
	urldate = {2021-08-26},
	journal = {ACM Transactions on Design Automation of Electronic Systems},
	author = {Huang, Guyue and Hu, Jingbo and He, Yifan and Liu, Jialong and Ma, Mingyuan and Shen, Zhaoyang and Wu, Juejian and Xu, Yuanfan and Zhang, Hengrui and Zhong, Kai and Ning, Xuefei and Ma, Yuzhe and Yang, Haoyu and Yu, Bei and Yang, Huazhong and Wang, Yu},
	month = jun,
	year = {2021},
	keywords = {\#broken, Electronic design automation, Jab/\#TDAES, machine learning, neural networks},
	pages = {40:1--40:46},
}

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