research-article

Routability-driven Power/Ground Network Optimization Based on Machine Learning

Authors:

Ping-Wei Huang,

Yao-Wen ChangAuthors Info & Claims

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

Article No.: 53, Pages 1 - 27

https://doi.org/10.1145/3587817

Published: 17 May 2023 Publication History

Abstract

The dynamic IR drop of a power/ground (PG) network is a critical problem in modern circuit designs. Excessive IR drop slows down circuit performance and causes potential functional failures. Most industrial practices tend to over-design the PG network for the dynamic IR drop constraints, reducing routing resources and incurring routing congestion. Existing machine learning-based approaches target only dynamic IR drop prediction without considering the routability affected by the P/G network. This article develops a machine learning-based method to solve the dynamic IR drop and routing resources tradeoffs. Our model can predict the two targets accurately by adopting a multi-task learning scheme, achieving a 0.99 high correlation coefficient. We show that our trained model is generalizable by testing different placement results. Our algorithm also achieves significant speedups of up to 29× compared to the time-consuming dynamic IR drop simulation by a leading commercial tool. Experimental results show that our algorithm can save about 13% routing resources without worsening the dynamic IR drop peak value.

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Cited By

Xian JXing YCai SLi WXiong XHu Z(2024)WCPNet: Jointly Predicting Wirelength, Congestion and Power for FPGA Using Multi-Task LearningACM Transactions on Design Automation of Electronic Systems10.1145/365617029:3(1-19)Online publication date: 3-May-2024
https://doi.org/10.1145/3656170

Index Terms

Routability-driven Power/Ground Network Optimization Based on Machine Learning
1. Hardware
  1. Electronic design automation
    1. Physical design (EDA)

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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

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Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 17 May 2023

Online AM: 25 March 2023

Accepted: 05 January 2023

Revised: 03 December 2022

Received: 14 April 2022

Published in TODAES Volume 28, Issue 4

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AnaGlobe, MediaTek, Synopsys, TSMC, MOST of Taiwan
NTU

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Cited By

Xian JXing YCai SLi WXiong XHu Z(2024)WCPNet: Jointly Predicting Wirelength, Congestion and Power for FPGA Using Multi-Task LearningACM Transactions on Design Automation of Electronic Systems10.1145/365617029:3(1-19)Online publication date: 3-May-2024
https://doi.org/10.1145/3656170

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