Customized Routing Optimization Based on Gradient Boost Regressor Model
This work addresses routing optimization in electronic design automation, but it appears incremental as it applies an existing method to a specific domain problem.
The paper tackled the limitation of current EDA tools by proposing a machine learning framework using a gradient boost regressor model to predict underestimated risky nets, achieving clear timing improvement and a trend toward timing closure.
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.