research-article

Pin Accessibility and Routing Congestion Aware DRC Hotspot Prediction Using Graph Neural Network and U-Net

Authors:

Kyeonghyeon Baek,

Taewhan KimAuthors Info & Claims

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

Article No.: 26, Pages 1 - 9

https://doi.org/10.1145/3508352.3549346

Published: 22 December 2022 Publication History

Abstract

An accurate DRC (design rule check) hotspot prediction at the placement stage is essential in order to reduce a substantial amount of design time required for the iterations of placement and routing. It is known that for implementing chips with advanced technology nodes, (1) pin accessibility and (2) routing congestion are two major causes of DRVs (design rule violations). Though many ML (machine learning) techniques have been proposed to address this prediction problem, it was not easy to assemble the aggregate data on items 1 and 2 in a unified fashion for training ML models, resulting in a considerable accuracy loss in DRC hotspot prediction. This work overcomes this limitation by proposing a novel ML based DRC hotspot prediction technique, which is able to accurately capture the combined impact of items 1 and 2 on DRC hotspots. Precisely, we devise a graph, called pin proximity graph, that effectively models the spatial information on cell I/O pins and the information on pin-to-pin disturbance relation. Then, we propose a new ML model, called PGNN, which tightly combines GNN (graph neural network) and U-net in a way that GNN is used to embed pin accessibility information abstracted from our pin proximity graph while U-net is used to extract routing congestion information from grid-based features. Through experiments with a set of benchmark designs using Nangate 15nm library, our PGNN outperforms the existing ML models on all benchmark designs, achieving on average 7.8~12.5% improvements on F1-score while taking 5.5× fast inference time in comparison with that of the state-of-the-art techniques.

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https://dl.acm.org/doi/10.1145/3626184.3633312
Chang KKim T(2024)Pre-route timing prediction and optimization with graph neural network modelsIntegration, the VLSI Journal10.1016/j.vlsi.2024.10226299:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.vlsi.2024.102262
Liu QLing MZhu YRui YWang R(2024)Preventing short violations in clock routing with an SVM classifier before powerplanning and placementMicroelectronics Journal10.1016/j.mejo.2024.106429153(106429)Online publication date: Nov-2024
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Pin Accessibility and Routing Congestion Aware DRC Hotspot Prediction Using Graph Neural Network and U-Net
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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.

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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IEEE-EDS: Electronic Devices Society
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New York, NY, United States

Publication History

Published: 22 December 2022

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

October 30 - November 3, 2022

California, San Diego

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Cited By

Lu CJiang IPeng CMohd Razha MUber AHui-Ru Jiang IPosser G(2024)Power Sub-Mesh Construction in Multiple Power Domain Design with IR Drop and Routability OptimizationProceedings of the 2024 International Symposium on Physical Design10.1145/3626184.3633312(205-212)Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1145/3626184.3633312
Chang KKim T(2024)Pre-route timing prediction and optimization with graph neural network modelsIntegration, the VLSI Journal10.1016/j.vlsi.2024.10226299:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.vlsi.2024.102262
Liu QLing MZhu YRui YWang R(2024)Preventing short violations in clock routing with an SVM classifier before powerplanning and placementMicroelectronics Journal10.1016/j.mejo.2024.106429153(106429)Online publication date: Nov-2024
https://doi.org/10.1016/j.mejo.2024.106429
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
Shin Y(2023)AI-EDA: Toward a Holistic Approach to AI-Powered EDA2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD)10.1109/MLCAD58807.2023.10299815(1-3)Online publication date: 10-Sep-2023
https://doi.org/10.1109/MLCAD58807.2023.10299815
Min KKwon SLee SKim DPark SKang S(2023)ClusterNet: Routing Congestion Prediction and Optimization Using Netlist Clustering and Graph Neural Networks2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323942(1-9)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323942
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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