Toward Reinforcement Learning-based Rectilinear Macro Placement Under Human Constraints
This addresses chip design automation for designers, but it is incremental as it builds on existing methods like Google's Circuit Training.
The study tackled macro placement in chip design with rectilinear shapes and human-like constraints, achieving power-performance-area metrics and placements comparable to human intervention.
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.