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PROS: a plug-in for routability optimization applied in the state-of-the-art commercial EDA tool using deep learning

Published: 17 December 2020 Publication History

Abstract

Recently the topic of routability optimization with prior knowledge obtained by machine learning techniques has been widely studied. However, limited by the prediction accuracy, the predictors of the existing related works can hardly be applied in a real-world EDA tool without extra runtime overhead for feature preparation. In this paper, we revisit this topic and propose a practical plug-in for routability optimization named PROS which can be applied in the state-of-the-art commercial EDA tool with negligible runtime overhead. PROS consists of an effective fully convolutional network (FCN) based predictor that only utilizes the data from placement result to forecast global routing (GR) congestion and a parameter optimizer that can reasonably adjust GR cost parameters based on prediction result to generate a better GR solution for detailed routing. Experiments on 19 industrial designs in advanced technology node show that PROS can achieve high accuracy of GR congestion prediction and significantly reduce design rule checking (DRC) violations by 11.65% on average.

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Cited By

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  • (2024)Effective Heterogeneous Graph Neural Network for Routing Congestion Prediction2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617734(369-373)Online publication date: 10-May-2024
  • (2024)Pre-route timing prediction and optimization with graph neural network modelsIntegration10.1016/j.vlsi.2024.102262(102262)Online publication date: Aug-2024
  • (2024)Performance Evaluation of GA, HS, PSO Algorithms for Optimizing Area, Wirelength Using MCNC ArchitecturesModern Approaches in Machine Learning and Cognitive Science: A Walkthrough10.1007/978-3-031-43009-1_5(53-70)Online publication date: 14-Jan-2024
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  1. PROS: a plug-in for routability optimization applied in the state-of-the-art commercial EDA tool using deep learning

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    cover image ACM Conferences
    ICCAD '20: Proceedings of the 39th International Conference on Computer-Aided Design
    November 2020
    1396 pages
    ISBN:9781450380263
    DOI:10.1145/3400302
    • General Chair:
    • Yuan Xie
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 17 December 2020

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    Cited By

    View all
    • (2024)Effective Heterogeneous Graph Neural Network for Routing Congestion Prediction2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617734(369-373)Online publication date: 10-May-2024
    • (2024)Pre-route timing prediction and optimization with graph neural network modelsIntegration10.1016/j.vlsi.2024.102262(102262)Online publication date: Aug-2024
    • (2024)Performance Evaluation of GA, HS, PSO Algorithms for Optimizing Area, Wirelength Using MCNC ArchitecturesModern Approaches in Machine Learning and Cognitive Science: A Walkthrough10.1007/978-3-031-43009-1_5(53-70)Online publication date: 14-Jan-2024
    • (2023)Progress of Placement Optimization for Accelerating VLSI Physical DesignElectronics10.3390/electronics1202033712:2(337)Online publication date: 9-Jan-2023
    • (2023)DTOC: integrating Deep-learning driven Timing Optimization into the state-of-the-art Commercial EDA tool2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE56975.2023.10137234(1-6)Online publication date: Apr-2023
    • (2023)Routability Prediction using Deep Hierarchical Classification and Regression2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE56975.2023.10136974(1-2)Online publication date: Apr-2023
    • (2023)CircuitNet: An Open-Source Dataset for Machine Learning in VLSI CAD Applications With Improved Domain-Specific Evaluation Metric and Learning StrategiesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.328797042:12(5034-5047)Online publication date: Dec-2023
    • (2023)Construction of Realistic Place-and-Route Benchmarks for Machine Learning ApplicationsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.320953042:6(2030-2042)Online publication date: Jun-2023
    • (2023)The Dark Side: Security and Reliability Concerns in Machine Learning for EDAIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319917242:4(1171-1184)Online publication date: Apr-2023
    • (2023)PROS 2.0: A Plug-In for Routability Optimization and Routed Wirelength Estimation Using Deep LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.316825942:1(164-177)Online publication date: Jan-2023
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