Machine Learning for Electronic Design Automation: A Survey

By Guyue Huang, Jingbo Hu, Yifan He, Jialong Liu, Mingyuan Ma, Zhaoyang Shen, Juejian Wu, Yuanfan Xu, Hengrui Zhang, Kai Zhong and 6 more. ACM Trans. Design Autom. Electr. Syst., 2021

346 citations recorded by the source corpus.

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.

Subfields: Electrical and Electronic Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/2e78331cea1db959d1dfe0458eacb284a191f369

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In Electrical and Electronic Engineering

1. Siddharth Garg
2. Ramesh Karri

Citations in the corpus

• Chip-Chat: Challenges and Opportunities in Conversational Hardware Design (2023, background): “As digital designs continue to grow in capability and complexity, software components in Integrated Circuit (IC) Computer Aided Design (CAD) have adopted machine learning (ML) throughout the Electronic Design Automation flow (e.g. [1]–[3]).”