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Toward Reinforcement Learning-based Rectilinear Macro Placement Under Human Constraints
- Publication Year :
- 2023
-
Abstract
- Macro placement is a critical phase in chip design, which becomes more intricate when involving general rectilinear macros and layout areas. Furthermore, macro placement that incorporates human-like constraints, such as design hierarchy and peripheral bias, has the potential to significantly reduce the amount of additional manual labor required from designers. This study proposes a methodology that leverages an approach suggested by Google's Circuit Training (G-CT) to provide a learning-based macro placer that not only supports placing rectilinear cases, but also adheres to crucial human-like design principles. Our experimental results demonstrate the effectiveness of our framework in achieving power-performance-area (PPA) metrics and in obtaining placements of high quality, comparable to those produced with human intervention. Additionally, our methodology shows potential as a generalized model to address diverse macro shapes and layout areas.<br />Comment: Fast ML for Science @ ICCAD 2023
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2311.03383
- Document Type :
- Working Paper