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- W2766335712 abstract "In this paper, we discussed limitation of current electronic-design-automoation (EDA) tool and proposed a machine learning framework to overcome the limitations and achieve better design quality. We explored how to efficiently extract relevant features and leverage gradient boost regressor (GBR) model to predict underestimated risky net (URN). Customized routing optimizations are applied to the URNs and results show clear timing improvement and trend to converge toward timing closure." @default.
- W2766335712 created "2017-11-10" @default.
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- W2766335712 date "2017-10-28" @default.
- W2766335712 modified "2023-09-27" @default.
- W2766335712 title "Customized Routing Optimization Based on Gradient Boost Regressor Model." @default.
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