Machine Learning for Electronic Design Automation: A Survey
This survey paper helps researchers and practitioners understand the landscape of ML applications in EDA, a field facing increasing complexity due to CMOS technology down-scaling.
This paper surveys the application of machine learning (ML) techniques in electronic design automation (EDA), organizing existing studies according to the EDA hierarchy.
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.