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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2407.01440 |
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| _version_ | 1866913411921608704 |
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| author | Onal, Bugra Dogan, Eren Khan, Muhammad Hadir Guthaus, Matthew R. |
| author_facet | Onal, Bugra Dogan, Eren Khan, Muhammad Hadir Guthaus, Matthew R. |
| contents | The Rectilinear Steiner Minimum Tree (RSMT) problem is a fundamental problem in VLSI placement and routing and is known to be NP-hard. Traditional RSMT algorithms spend a significant amount of time on finding Steiner points to reduce the total wire length or use heuristics to approximate producing sub-optimal results. We show that Graph Neural Networks (GNNs) can be used to predict optimal Steiner points in RSMTs with high accuracy and can be parallelized on GPUs. In this paper, we propose GAT-Steiner, a graph attention network model that correctly predicts 99.846% of the nets in the ISPD19 benchmark with an average increase in wire length of only 0.480% on suboptimal wire length nets. On randomly generated benchmarks, GAT-Steiner correctly predicts 99.942% with an average increase in wire length of only 0.420% on suboptimal wire length nets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01440 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GAT-Steiner: Rectilinear Steiner Minimal Tree Prediction Using GNNs Onal, Bugra Dogan, Eren Khan, Muhammad Hadir Guthaus, Matthew R. Machine Learning The Rectilinear Steiner Minimum Tree (RSMT) problem is a fundamental problem in VLSI placement and routing and is known to be NP-hard. Traditional RSMT algorithms spend a significant amount of time on finding Steiner points to reduce the total wire length or use heuristics to approximate producing sub-optimal results. We show that Graph Neural Networks (GNNs) can be used to predict optimal Steiner points in RSMTs with high accuracy and can be parallelized on GPUs. In this paper, we propose GAT-Steiner, a graph attention network model that correctly predicts 99.846% of the nets in the ISPD19 benchmark with an average increase in wire length of only 0.480% on suboptimal wire length nets. On randomly generated benchmarks, GAT-Steiner correctly predicts 99.942% with an average increase in wire length of only 0.420% on suboptimal wire length nets. |
| title | GAT-Steiner: Rectilinear Steiner Minimal Tree Prediction Using GNNs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.01440 |