research-article

Power Grid Reduction by Sparse Convex Optimization

Authors:

David Z. PanAuthors Info & Claims

ISPD '18: Proceedings of the 2018 International Symposium on Physical Design

Pages 60 - 67

https://doi.org/10.1145/3177540.3178247

Published: 25 March 2018 Publication History

Abstract

With the dramatic increase in the complexity of modern integrated circuits (ICs), direct analysis and verification of IC power distribution networks (PDNs) have become extremely computationally expensive. Various power grid reduction methods are proposed to reduce the grid size for fast verification and simulation but usually suffer from poor scalability. In this paper, we present a convex optimization-based framework for power grid reduction. Edge sparsification is formulated as a weighted convex optimization problem with sparsity-inducing penalties, which provides an accurate control over the final error. A greedy coordinate descent (GCD) method with optimality guarantee is proposed along with a novel coordinate selection strategy to improve the efficiency and accuracy of edge sparsification. Experimental results demonstrate that the proposed approach achieves better performance compared with traditional gradient descent methods, and 98% accuracy and good sparsity for industrial benchmarks.

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Cited By

Liu ZYu W(2023)Accuracy-Preserving Reduction of Sparsified Reduced Power Grids with A Multilevel Node Aggregation Scheme2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323865(1-9)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323865
Zhang CZhou P(2020)Improved Hierarchical IR Drop Analysis in Homogeneous Circuits2020 IEEE 15th International Conference on Solid-State & Integrated Circuit Technology (ICSICT)10.1109/ICSICT49897.2020.9278267(1-3)Online publication date: 3-Nov-2020
https://doi.org/10.1109/ICSICT49897.2020.9278267

Index Terms

Power Grid Reduction by Sparse Convex Optimization
1. Hardware
  1. Robustness
    1. Design for manufacturability
      1. Process variations
      2. Yield and cost modeling
  2. Very large scale integration design
    1. VLSI design manufacturing considerations

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cover image ACM Conferences

ISPD '18: Proceedings of the 2018 International Symposium on Physical Design

March 2018

178 pages

ISBN:9781450356268

DOI:10.1145/3177540

General Chair:
Chris Chu
Iowa State University
,
Program Chair:
Ismail Bustany
Xilinx Inc.

Copyright © 2018 ACM.

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SIGDA: ACM Special Interest Group on Design Automation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 March 2018

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ISPD '18: International Symposium on Physical Design

March 25 - 28, 2018

California, Monterey, USA

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Overall Acceptance Rate 62 of 172 submissions, 36%

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Cited By

Liu ZYu W(2023)Accuracy-Preserving Reduction of Sparsified Reduced Power Grids with A Multilevel Node Aggregation Scheme2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323865(1-9)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323865
Zhang CZhou P(2020)Improved Hierarchical IR Drop Analysis in Homogeneous Circuits2020 IEEE 15th International Conference on Solid-State & Integrated Circuit Technology (ICSICT)10.1109/ICSICT49897.2020.9278267(1-3)Online publication date: 3-Nov-2020
https://doi.org/10.1109/ICSICT49897.2020.9278267

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