Simultaneous Timing Driven Tree Surgery in Routing with Machine Learning-based Acceleration
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摘要In global routing, both timing and routability are critical criterions to measure the performance of a design. However, these two objectives naturally conflict with each other during routing. In this paper, a tree surgery technique is presented to adjust routing tree topologies in global routing to fix timing. We formulate the problem as a quadratic program (QP), which adjusts routing topologies of all the nets from a global perspective and takes congestion into consideration to trade off timing and routability objectives. We also apply machine learning-based techniques to accelerate our algorithm, which offers a fast and effective way to solve the problem. Experimental results on ICCAD 2015 benchmarks show that our algorithms can achieve 10.12% timing improvement with no significant degradation in routability and wirelength. With machine learning-based acceleration (MLA), our results can be obtained in almost negligible runtime.
出版社接受日期22.02.2018
著者Peishan Tu, Chak-Wa Pui, Evangeline F.Y. Young
會議名稱ACM Great Lakes Symposium on VLSI (GLSVLSI 2018)
會議開始日23.05.2018
會議完結日25.05.2018
會議地點Chicago
會議國家/地區美國
出版年份2018
語言美式英語
關鍵詞Timing, Routing

上次更新時間 2018-25-06 於 10:55