期刊
IEEE DESIGN & TEST
卷 40, 期 1, 页码 17-33出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MDAT.2022.3216799
关键词
Integrated circuit modeling; Predictive models; Integrated circuits; Feature extraction; Physical design; Layout; Computational modeling; Machine learning; Electronic design automation; Computer-aided design
This article discusses the potential of machine learning methods in addressing the challenges posed by the increasing complexity and size of design space in integrated circuit (IC) design. It provides a comprehensive survey of the current state of knowledge in both IC design problems and ML-based solutions. The article also summarizes the open problems at the intersection of advanced IC design and ML.
The increasing complexity and size of design space poses significant challenges for integrated circuit (IC) design. This article discusses the potential of machine learning (ML) methods to address these challenges and provides a comprehensive survey of the current state of knowledge along both IC design problems and ML-based solutions. The article also summarizes the open problems at the intersection of advanced IC design and ML.
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