Machine Learning for Electronic Design Automation: A Survey

被引：170

作者：

Huang, Guyue ^{[1
]}

Hu, Jingbo ^{[1
]}

He, Yifan ^{[1
]}

Liu, Jialong ^{[1
]}

Ma, Mingyuan ^{[1
]}

Shen, Zhaoyang ^{[1
]}

Wu, Juejian ^{[1
]}

Xu, Yuanfan ^{[1
]}

Zhang, Hengrui ^{[1
]}

Zhong, Kai ^{[1
]}

Ning, Xuefei ^{[1
]}

Ma, Yuzhe ^{[2
]}

Yang, Haoyu ^{[2
]}

Yu, Bei ^{[2
]}

Yang, Huazhong ^{[1
]}

Wang, Yu ^{[1
]}

机构：

[1] Tsinghua Univ, Beijing, Peoples R China

[2] Chinese Univ Hong Kong, Hong Kong, Peoples R China

来源：

ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS | 2021年 / 26卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Electronic design automation; machine learning; neural networks; DIRECTED TEST-GENERATION; NEURAL-NETWORKS; VERIFICATION; ANALOG; RF; OPTIMIZATION; PREDICTION; QUALITY;

D O I：

10.1145/3451179

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：46

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