FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913322461298688 |
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| author | Wang, Shang Mamillapalli, Deepak Ranganatha Sastry Yang, Tianpei Taylor, Matthew E. |
| author_facet | Wang, Shang Mamillapalli, Deepak Ranganatha Sastry Yang, Tianpei Taylor, Matthew E. |
| contents | This paper introduces the problem of learning to place logic blocks in Field-Programmable Gate Arrays (FPGAs) and a learning-based method. In contrast to previous search-based placement algorithms, we instead employ Reinforcement Learning (RL) with the goal of minimizing wirelength. In addition to our preliminary learning results, we also evaluated a novel decomposition to address the nature of large search space when placing many blocks on a chipboard. Empirical experiments evaluate the effectiveness of the learning and decomposition paradigms on FPGA placement tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13061 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning Wang, Shang Mamillapalli, Deepak Ranganatha Sastry Yang, Tianpei Taylor, Matthew E. Hardware Architecture Artificial Intelligence Machine Learning This paper introduces the problem of learning to place logic blocks in Field-Programmable Gate Arrays (FPGAs) and a learning-based method. In contrast to previous search-based placement algorithms, we instead employ Reinforcement Learning (RL) with the goal of minimizing wirelength. In addition to our preliminary learning results, we also evaluated a novel decomposition to address the nature of large search space when placing many blocks on a chipboard. Empirical experiments evaluate the effectiveness of the learning and decomposition paradigms on FPGA placement tasks. |
| title | FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning |
| topic | Hardware Architecture Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2404.13061 |