  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.
