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.
@article{arxiv.2102.03357,
title = {Machine Learning for Electronic Design Automation: A Survey},
author = {Guyue Huang and Jingbo Hu and Yifan He and Jialong Liu and Mingyuan Ma and Zhaoyang Shen and Juejian Wu and Yuanfan Xu and Hengrui Zhang and Kai Zhong and Xuefei Ning and Yuzhe Ma and Haoyu Yang and Bei Yu and Huazhong Yang and Yu Wang},
journal= {arXiv preprint arXiv:2102.03357},
year = {2021}
}
Comments
Accepted by TODAES. The first 10 authors are ordered alphabetically