research-article

Open access

Placement Optimization via PPA-Directed Graph Clustering

Authors:

Haoxing RenAuthors Info & Claims

MLCAD '22: Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD

Pages 1 - 6

https://doi.org/10.1145/3551901.3556482

Published: 12 September 2022 Publication History

Abstract

In this paper, we present the first Power, Performance, and Area (PPA)-directed, end-to-end placement optimization framework that provides cell clustering constraints as placement guidance to advance commercial placers. Specifically, we formulate PPA metrics as Machine Learning (ML) loss functions, and use graph clustering techniques to optimize them by improving clustering assignments. Experimental results on 5 GPU/CPU designs in a 5nm technology not only show that our framework immediately improves the PPA metrics at the placement stage, but also demonstrate that the improvements last firmly to the post-route stage, where we observe improvements of 89% in total negative slack (TNS), 26% in effective frequency, 2.4% in wirelength, and 1.4% in clock power.

References

[1]

Anthony Agnesina, Kyungwook Chang, and Sung Kyu Lim. Vlsi placement parameter optimization using deep reinforcement learning. In Proceedings of the 39th International Conference on Computer-Aided Design, pages 1--9, 2020.

Digital Library

[2]

Xiang Gao, Yi-Min Jiang, Lixin Shao, Pedja Raspopovic, Menno E Verbeek, Manish Sharma, Vineet Rashingkar, and Amit Jalota. Congestion and timing aware macro placement using machine learning predictions from different data sources: Crossdesign model applicability and the discerning ensemble. In Proceedings of the 2022 International Symposium on Physical Design, pages 195--202, 2022.

Digital Library

[3]

Zizheng Guo, Mingjie Liu, Jiaqi Gu, Shuhan Zhang, David Z. Pan, and Yibo Lin. A timing engine inspired graph neural network model for pre-routing slack prediction. In Proceedings of the 59th Annual Design Automation Conference 2022. ACM, 2022.

Digital Library

[4]

Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Advances in neural information processing systems, 30, 2017.

[5]

Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.

[6]

Solomon Kullback and Richard A Leibler. On information and sufficiency. The annals of mathematical statistics, 22(1):79--86, 1951.

[7]

Stuart Lloyd. Least squares quantization in pcm. IEEE transactions on information theory, 28(2):129--137, 1982.

Digital Library

[8]

Yi-Chen Lu, Sai Pentapati, and Sung Kyu Lim. The law of attraction: Affinityaware placement optimization using graph neural networks. In Proceedings of the 2021 International Symposium on Physical Design, pages 7--14, 2021.

Digital Library

[9]

Yi-Chen Lu, Sai Surya Kiran Pentapati, Lingjun Zhu, Kambiz Samadi, and Sung Kyu Lim. Tp-gnn: A graph neural network framework for tier partitioning in monolithic 3d ics. In 2020 57th ACM/IEEE Design Automation Conference (DAC), pages 1--6. IEEE, 2020.

[10]

Azade Nazi, Will Hang, Anna Goldie, Sujith Ravi, and Azalia Mirhoseini. Gap: Generalizable approximate graph partitioning framework. arXiv:1903.00614, 2019.

[11]

Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084, 2019.

[12]

Claude Elwood Shannon. A mathematical theory of communication. The Bell system technical journal, 27(3):379--423, 1948.

[13]

Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008.

[14]

Ziyi Wang, Chen Bai, Zhuolun He, Guangliang Zhang, Qiang Xu, Tsung-Yi Ho, Bei Yu, and Yu Huang. Functionality matters in netlist representation learning. In Proceedings of the 59th Annual Design Automation Conference 2022. ACM, 2022.

Digital Library

[15]

Junyuan Xie, Ross Girshick, and Ali Farhadi. Unsupervised deep embedding for clustering analysis. In International conference on machine learning. PMLR, 2016.

Cited By

Zhang RRajarathnam RPan DKoushanfar FAmrouch HHu JGarg SLin Y(2024)Automated Physical Design Watermarking Leveraging Graph Neural NetworksProceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD10.1145/3670474.3685951(1-10)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3670474.3685951
Kahng AKang SKundu SMin KPark SPramanik BDe V(2024)PPA-Relevant Clustering-Driven Placement for Large-Scale VLSI DesignsProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3655991(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3655991
Zhang RRajarathnam RPan DKoushanfar F(2024)Automated Physical Design Watermarking Leveraging Graph Neural Networks2024 ACM/IEEE 6th Symposium on Machine Learning for CAD (MLCAD)10.1109/MLCAD62225.2024.10740234(1-10)Online publication date: 9-Sep-2024
https://doi.org/10.1109/MLCAD62225.2024.10740234
Show More Cited By

Index Terms

Placement Optimization via PPA-Directed Graph Clustering
1. Hardware
  1. Electronic design automation
    1. Physical design (EDA)
      1. Placement

Recommendations

PPA-Relevant Clustering-Driven Placement for Large-Scale VLSI Designs
DAC '24: Proceedings of the 61st ACM/IEEE Design Automation Conference

Today's place-and-route (P&R) flows are increasingly challenged by complexity and scale of modern designs. Often, heuristics must trade off between turnaround time and quality of PPA outcomes. This paper presents a clustered placement methodology that ...
Net-based force-directed macrocell placement for wirelength optimization

We propose a net-based hierarchical macrocell placement such that "net placement" dictates the cell placement. The proposed approach has four phases. 1) Net clustering and net-level floorplanning phase: a weighted net dependency graph is built from the ...
Wirelength-driven force-directed 3D FPGA placement
GLSVLSI '10: Proceedings of the 20th symposium on Great lakes symposium on VLSI

In this paper, a wirelength-driven force-directed three-dimension (3-D) Field Programmable Gate Arrays (FPGA) placement algorithm (3D-WFP) is presented. The algorithm is composed of three stages: Overlap permitted force-directed 2-D placement, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MLCAD '22: Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD

September 2022

181 pages

ISBN:9781450394864

DOI:10.1145/3551901

General Chairs:
Paul Franzon
North Carolina State University, USA
,
Andrew B. Kahng
University of California at San Diego, USA
,
Program Chairs:
Hai (Helen) Li
Duke University, USA
,
Bing Li
Technical University of Munich, Germany

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

SIGDA: ACM Special Interest Group on Design Automation
IEEE CEDA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 September 2022

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MLCAD '22

Sponsor:

SIGDA

MLCAD '22: 2022 ACM/IEEE Workshop on Machine Learning for CAD

September 12 - 13, 2022

Virtual Event, China

Acceptance Rates

Overall Acceptance Rate 35 of 83 submissions, 42%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
1,516
Total Downloads

Downloads (Last 12 months)612
Downloads (Last 6 weeks)90

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang RRajarathnam RPan DKoushanfar FAmrouch HHu JGarg SLin Y(2024)Automated Physical Design Watermarking Leveraging Graph Neural NetworksProceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD10.1145/3670474.3685951(1-10)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3670474.3685951
Kahng AKang SKundu SMin KPark SPramanik BDe V(2024)PPA-Relevant Clustering-Driven Placement for Large-Scale VLSI DesignsProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3655991(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3655991
Zhang RRajarathnam RPan DKoushanfar F(2024)Automated Physical Design Watermarking Leveraging Graph Neural Networks2024 ACM/IEEE 6th Symposium on Machine Learning for CAD (MLCAD)10.1109/MLCAD62225.2024.10740234(1-10)Online publication date: 9-Sep-2024
https://doi.org/10.1109/MLCAD62225.2024.10740234
Lai YLiu JTang ZWang BHao JLuo PKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)ChiPFormerProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619165(18346-18364)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619165
Lu YRen HHsiao HLim SChinnery DHui-Ru Jiang I(2023)DREAM-GANProceedings of the 2023 International Symposium on Physical Design10.1145/3569052.3572993(141-148)Online publication date: 26-Mar-2023
https://dl.acm.org/doi/10.1145/3569052.3572993
Lu YLim SMitra TYoung EXiong J(2022)On Advancing Physical Design Using Graph Neural NetworksProceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design10.1145/3508352.3561094(1-7)Online publication date: 30-Oct-2022
https://dl.acm.org/doi/10.1145/3508352.3561094

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents