1. MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper
- Author
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David Z. Pan, Yibo Lin, Jorg Henkel, Marilyn Wolf, Martin Rapp, Bei Yu, and Hussam Amrouch
- Subjects
business.industry ,Computer science ,Heuristic (computer science) ,Reliability (computer networking) ,media_common.quotation_subject ,DATA processing & computer science ,Brute-force search ,CAD ,02 engineering and technology ,Machine learning ,computer.software_genre ,Computer Graphics and Computer-Aided Design ,020202 computer hardware & architecture ,Set (abstract data type) ,Open research ,0202 electrical engineering, electronic engineering, information engineering ,Quality (business) ,Artificial intelligence ,Configuration space ,Electrical and Electronic Engineering ,ddc:004 ,business ,computer ,Software ,media_common - Abstract
Due to the increasing size of s (s), their design and optimization phases (i.e., ) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some trade-off between design quality and computational overhead. ML can build powerful models that have successfully been employed in related domains. In this survey, we categorize how () may be used and is used for design-time and run-time optimization and exploration strategies of s. A meta-study of published techniques unveils areas in that are well-explored and underexplored with, as well as trends in the employed algorithms. We present a comprehensive categorization and summary of the state of the art on for. Finally, we summarize remaining challenges and promising open research directions.
- Published
- 2022
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