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Toward Reinforcement Learning-based Rectilinear Macro Placement Under Human Constraints

2023-11-03 · 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.

📄 PDF Abstract BibTeX arXiv:2311.03383

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reinforcement-learning Reinforcement Learning

One Step Beyond: Feedthrough & Placement-Aware Rectilinear Floorplanner

2025-07-20 · Zhexuan Xu, Jie Wang, Siyuan Xu, Zijie Geng 외 arxiv

Floorplanning determines the shapes and locations of modules on a chip canvas and plays a critical role in optimizing the chip's Power, Performance, and Area (PPA) metrics. However, existing floorplanning approaches ofte…

Delving into Macro Placement with Reinforcement Learning

2021-09-06 · Zixuan Jiang, Ebrahim Songhori, Shen Wang, Anna Goldie 외

In physical design, human designers typically place macros via trial and error, which is a Markov decision process. Reinforcement learning (RL) methods have demonstrated superhuman performance on the macro placement. In …

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

On Joint Learning for Solving Placement and Routing in Chip Design

2021-10-30 · NeurIPS 2021 12 · Ruoyu Cheng, Junchi Yan

For its advantage in GPU acceleration and less dependency on human experts, machine learning has been an emerging tool for solving the placement and routing problems, as two critical steps in modern chip design flow. Bei…

GPU reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Non-Overlapping Placement of Macro Cells based on Reinforcement Learning in Chip Design

2024-07-26 · Tao Yu, Peng Gao, Fei Wang, Ru-Yue Yuan

Due to the increasing complexity of chip design, existing placement methods still have many shortcomings in dealing with macro cells coverage and optimization efficiency. Aiming at the problems of layout overlap, inferio…

Assessment of Reinforcement Learning for Macro Placement

2023-02-21 · Chung-Kuan Cheng, Andrew B. Kahng, Sayak Kundu, Yucheng Wang 외

We provide open, transparent implementation and assessment of Google Brain's deep reinforcement learning approach to macro placement and its Circuit Training (CT) implementation in GitHub. We implement in open source key…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)