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Delving into Macro Placement with Reinforcement Learning

2021-09-06 · Zixuan Jiang, Ebrahim Songhori, Shen Wang, Anna Goldie, Azalia Mirhoseini, Joe Jiang, Young-Joon Lee, David Z. Pan

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 this paper, we propose an extension to this prior work (Mirhoseini et al., 2020). We first describe the details of the policy and value network architecture. We replace the force-directed method with DREAMPlace for placing standard cells in the RL environment. We also compare our improved method with other academic placers on public benchmarks.

📄 PDF Abstract BibTeX arXiv:2109.02587

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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…

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)

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)

Toward Reinforcement Learning-based Rectilinear Macro Placement Under Human Constraints

2023-11-03 · Tuyen P. Le, Hieu T. Nguyen, Seungyeol Baek, Taeyoun Kim 외

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 …

reinforcement-learning Reinforcement Learning

Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer

2024-12-10 · Ke Xue, Ruo-Tong Chen, Xi Lin, Yunqi Shi 외

In modern chip design, placement aims at placing millions of circuit modules, which is an essential step that significantly influences power, performance, and area (PPA) metrics. Recently, reinforcement learning (RL) has…