Customized Routing Optimization Based on Gradient Boost Regressor Model

2017/10/28 by Zheng Chen, Zheng, Chen, Clara Grzegorz Kasprowicz +3 · 3 citations
Computer Science · #Text and Document Classification Technologies #Web Data Mining and Analysis #Algorithms and Data Compression

paper · pdf · doi:10.48550/arxiv.1710.11118

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.

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