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A Stochastic Approach to Handle Non-Determinism in Deep Learning-Based Design Rule Violation Predictions

Authors:

Rongjian Liang,

Gi-Joon NamAuthors Info & Claims

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

Article No.: 64, Pages 1 - 8

https://doi.org/10.1145/3508352.3549347

Published: 22 December 2022 Publication History

Abstract

Deep learning is a promising approach to early DRV (Design Rule Violation) prediction. However, non-deterministic parallel routing hampers model training and degrades prediction accuracy. In this work, we propose a stochastic approach, called LGC-Net, to solve this problem. In this approach, we develop new techniques of Gaussian random field layer and focal likelihood loss function to seamlessly integrate Log Gaussian Cox process with deep learning. This approach provides not only statistical regression results but also classification ones with different thresholds without retraining. Experimental results with noisy training data on industrial designs demonstrate that LGC-Net achieves significantly better accuracy of DRV density prediction than prior arts.

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Digital Library

Cited By

Yu B(2023)Machine Learning in EDA: When and How2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD)10.1109/MLCAD58807.2023.10299822(1-6)Online publication date: 10-Sep-2023
https://doi.org/10.1109/MLCAD58807.2023.10299822
Zheng SZou LXu PLiu SYu BWong M(2023)Lay-Net: Grafting Netlist Knowledge on Layout-Based Congestion Prediction2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323800(1-9)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323800
Park SKim DKwon SKang S(2023)Routability Prediction and Optimization Using Explainable AI2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323630(1-8)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323630

Index Terms

A Stochastic Approach to Handle Non-Determinism in Deep Learning-Based Design Rule Violation Predictions
1. Hardware
  1. Electronic design automation
    1. Physical design (EDA)

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Published In

cover image ACM Conferences

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

October 2022

1467 pages

ISBN:9781450392174

DOI:10.1145/3508352

Conference Chair:
Tulika Mitra
National University of Singapore
,
Program Chairs:
Evangeline Young
The Chinese University of Hong Kong
,
Jinjun Xiong
University at Buffalo (UB)

Copyright © 2022 ACM.

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SIGDA: ACM Special Interest Group on Design Automation

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IEEE-EDS: Electronic Devices Society
IEEE CAS
IEEE CEDA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 December 2022

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ICCAD '22

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SIGDA

ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design

October 30 - November 3, 2022

California, San Diego

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Overall Acceptance Rate 457 of 1,762 submissions, 26%

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Cited By

Yu B(2023)Machine Learning in EDA: When and How2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD)10.1109/MLCAD58807.2023.10299822(1-6)Online publication date: 10-Sep-2023
https://doi.org/10.1109/MLCAD58807.2023.10299822
Zheng SZou LXu PLiu SYu BWong M(2023)Lay-Net: Grafting Netlist Knowledge on Layout-Based Congestion Prediction2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323800(1-9)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323800
Park SKim DKwon SKang S(2023)Routability Prediction and Optimization Using Explainable AI2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323630(1-8)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323630

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