research-article

Worst-case Power Integrity Prediction Using Convolutional Neural Network

Authors:

Cheng ZhuoAuthors Info & Claims

ACM Transactions on Design Automation of Electronic Systems, Volume 28, Issue 4

Article No.: 54, Pages 1 - 19

https://doi.org/10.1145/3564932

Published: 17 May 2023 Publication History

Abstract

Power integrity analysis is an essential step in power distribution network (PDN) sign-off to ensure the performance and reliability of chips. However, with the growing PDN size and increasing scenarios to be validated, it becomes very time- and resource-consuming to conduct full-stack PDN simulation to check the power integrity for different test vectors. Recently, various works have proposed machine learning–based methods for PDN power integrity prediction, many of which still suffer from large training overhead, inefficiency, or non-scalability. Thus, this article proposed an efficient and scalable framework for the worst-case power integrity prediction, which can handle general tasks including dynamic noise prediction and bump current prediction. The framework first reduces the spatial and temporal redundancy in the PDN and input current vector and then employs efficient feature extraction as well as a novel convolutional neural network architecture to predict the worst-case power integrity. Experimental results show that the proposed framework consistently outperforms the commercial tool and the state-of-the-art machine learning method with only 0.63–1.02% mean relative error and 25–69× speedup for noise prediction and 0.22–1.06% mean relative error and 24–64× speedup for bump current prediction.

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Cited By

Chen ZGuo CSong ZFeng GWang SZhang LYin XWu ZYan ZZhuo CNakamura YWang Y(2025)Invited Paper: Boosting Standard Cell Library Characterization with Machine LearningProceedings of the 30th Asia and South Pacific Design Automation Conference10.1145/3658617.3703638(385-391)Online publication date: 20-Jan-2025
https://dl.acm.org/doi/10.1145/3658617.3703638
Jiang YSong JYin XDong XSun SLin YJin ZYang XZhuo CAmrouch HHu JGarg SLin Y(2024)A Parallel Simulation Framework Incorporating Machine Learning-Based Hotspot Detection for Accelerated Power Grid AnalysisProceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD10.1145/3670474.3685947(1-7)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3670474.3685947
Jiang YSong JYang XDong XSun SLin YJin ZYin XZhuo C(2024)A Parallel Simulation Framework Incorporating Machine Learning-Based Hotspot Detection for Accelerated Power Grid Analysis2024 ACM/IEEE 6th Symposium on Machine Learning for CAD (MLCAD)10.1109/MLCAD62225.2024.10740202(1-7)Online publication date: 9-Sep-2024
https://doi.org/10.1109/MLCAD62225.2024.10740202

Index Terms

Worst-case Power Integrity Prediction Using Convolutional Neural Network
1. Computing methodologies
  1. Machine learning
2. Hardware
  1. Hardware validation
    1. Physical verification
      1. Power and thermal analysis

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Published In

cover image ACM Transactions on Design Automation of Electronic Systems

ACM Transactions on Design Automation of Electronic Systems Volume 28, Issue 4

July 2023

432 pages

ISSN:1084-4309

EISSN:1557-7309

DOI:10.1145/3597460

Editor:
X. Sharon Hu
University of Notre Dame, USA

Issue’s Table of Contents

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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Association for Computing Machinery

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 17 May 2023

Online AM: 03 October 2022

Accepted: 13 September 2022

Revised: 07 August 2022

Received: 26 April 2022

Published in TODAES Volume 28, Issue 4

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National Key R&D Program of China
Zhejiang Provincial Key R&D program
National Natural Science Foundation of China
Guangdong Provincial Key R&D program
Anhui Provincial Natural Science Foundation

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Cited By

Chen ZGuo CSong ZFeng GWang SZhang LYin XWu ZYan ZZhuo CNakamura YWang Y(2025)Invited Paper: Boosting Standard Cell Library Characterization with Machine LearningProceedings of the 30th Asia and South Pacific Design Automation Conference10.1145/3658617.3703638(385-391)Online publication date: 20-Jan-2025
https://dl.acm.org/doi/10.1145/3658617.3703638
Jiang YSong JYin XDong XSun SLin YJin ZYang XZhuo CAmrouch HHu JGarg SLin Y(2024)A Parallel Simulation Framework Incorporating Machine Learning-Based Hotspot Detection for Accelerated Power Grid AnalysisProceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD10.1145/3670474.3685947(1-7)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3670474.3685947
Jiang YSong JYang XDong XSun SLin YJin ZYin XZhuo C(2024)A Parallel Simulation Framework Incorporating Machine Learning-Based Hotspot Detection for Accelerated Power Grid Analysis2024 ACM/IEEE 6th Symposium on Machine Learning for CAD (MLCAD)10.1109/MLCAD62225.2024.10740202(1-7)Online publication date: 9-Sep-2024
https://doi.org/10.1109/MLCAD62225.2024.10740202
Chen YGuo ZWang RHuang RLin YZhuo C(2023)Dynamic Supply Noise Aware Timing Analysis With JIT Machine Learning IntegrationIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.334260343:5(1511-1524)Online publication date: 13-Dec-2023
https://dl.acm.org/doi/10.1109/TCAD.2023.3342603

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