Simultaneous Timing Driven Tree Surgery in Routing with Machine Learning-based Acceleration
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AbstractIn 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.
Acceptance Date22/02/2018
All Author(s) ListPeishan Tu, Chak-Wa Pui, Evangeline F.Y. Young
Name of ConferenceACM Great Lakes Symposium on VLSI (GLSVLSI 2018)
Start Date of Conference23/05/2018
End Date of Conference25/05/2018
Place of ConferenceChicago
Country/Region of ConferenceUnited States of America
Year2018
LanguagesEnglish-United States
KeywordsTiming, Routing

Last updated on 2018-25-06 at 10:55