Delving into Macro Placement with Reinforcement Learning
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)...
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| creator | Jiang, Zixuan Songhori, Ebrahim Wang, Shen Goldie, Anna Mirhoseini, Azalia Jiang, Joe Lee, Young-Joon Pan, David Z |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2109.02587 |
| format | Article |
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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
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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
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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.</abstract><doi>10.48550/arxiv.2109.02587</doi><oa>free_for_read</oa></addata></record> |
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| title | Delving into Macro Placement with Reinforcement Learning |
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