MLTDRC: Machine learning driven faster timing design rule check convergence
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Published:2023-10
Issue:
Volume:5
Page:100070
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ISSN:2773-0646
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Container-title:Memories - Materials, Devices, Circuits and Systems
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language:en
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Short-container-title:Memories - Materials, Devices, Circuits and Systems
Reference20 articles.
1. A.B. Kahng, Machine learning applications in physical design: recent results and directions, in: Proc. of ACM ISPD, 2018, pp. 68–73. 2. A.B. Kahng, New directions for learning-based IC design tools and methodologies, in: Proc. of IEEE ASP-DAC, 2018, pp. 405–410. 3. A.B. Kahng, U. Mallappa, L. Saul, Using machine learning to predict path-based slack from graph-based timing analysis, in: Proc. of IEEE ICCD, 2018, pp. 603–612. 4. Y. Ye, et al., Graph-learning-driven path-based timing analysis results predictor from graph-based timing analysis, in: Proc. of ACM ASPDAC, 2023, pp. 547–552. 5. A.B. Kahng, et al., Unobsrved corner prediction: reducing timing analysis effort for faster design convergence in advanced-node design, in: Proc. of IEEE DATE, 2019, pp. 168–173.
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