An Open-source End-to-End Logic Optimization Framework for Large-scale Boolean Network with Reinforcement Learning
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914728330133504 |
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| author | Li, Zhen Zhu, Kaixiang Zhou, Xuegong Wang, Lingli |
| author_facet | Li, Zhen Zhu, Kaixiang Zhou, Xuegong Wang, Lingli |
| contents | We propose an open-source end-to-end logic optimization framework for large-scale boolean network with reinforcement learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17395 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Open-source End-to-End Logic Optimization Framework for Large-scale Boolean Network with Reinforcement Learning Li, Zhen Zhu, Kaixiang Zhou, Xuegong Wang, Lingli Artificial Intelligence We propose an open-source end-to-end logic optimization framework for large-scale boolean network with reinforcement learning. |
| title | An Open-source End-to-End Logic Optimization Framework for Large-scale Boolean Network with Reinforcement Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2403.17395 |