Machine Learning for Electronic Design Automation: A Survey

• URL: http://arxiv.org/abs/2102.03357v2
• Date: Mon, 8 Mar 2021 08:18:35 GMT
• Title: Machine Learning for Electronic Design Automation: A Survey
• Authors: Guyue Huang, Jingbo Hu, Yifan He, Jialong Liu, Mingyuan Ma, Zhaoyang Shen, Juejian Wu, Yuanfan Xu, Hengrui Zhang, Kai Zhong, Xuefei Ning, Yuzhe Ma, Haoyu Yang, Bei Yu, Huazhong Yang, Yu Wang
• Abstract summary: With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. The recent breakthrough of machine learning (ML) and the increasing complexity of EDA tasks have aroused more interests in incorporating ML to solve EDA tasks.
• Score: 23.803190584543863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. Although the application of machine learning (ML) techniques in electronic design automation (EDA) can trace its history back to the 90s, the recent breakthrough of ML and the increasing complexity of EDA tasks have aroused more interests in incorporating ML to solve EDA tasks. In this paper, we present a comprehensive review of existing ML for EDA studies, organized following the EDA hierarchy.

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