Toward Reinforcement Learning-based Rectilinear Macro Placement Under
  Human Constraints
Tuyen P. Le Hieu T. Nguyen Seungyeol Baek Taeyoun Kim Jungwoo Lee Seongjung Kim Hyunjin Kim Misu Jung Daehoon Kim Seokyong Lee Daewoo Choi
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.