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Learning-based prediction of package power delivery network quality

Published: 21 January 2019 Publication History

Abstract

Power Delivery Network (PDN) is a critical component in modern System-on-Chip (SoC) designs. With the rapid development in applications, the quality of PDN, especially Package (PKG) PDN, determines whether a sufficient amount of power can be delivered to critical computing blocks. In conventional PKG design, PDN design typically takes multiple weeks including many manual iterations for optimization. Also, there is a large discrepancy between (i) quick simulation tools used for quick PDN quality assessment during the design phase, and (ii) the golden extraction tool used for signoff. This discrepancy may introduce more iterations. In this work, we propose a learning-based methodology to perform PKG PDN quality assessment both before layout (when only bump/ball maps, but no package routing, are available) and after layout (when routing is completed but no signoff analysis has been launched). Our contributions include (i) identification of important parameters to estimate the achievable PKG PDN quality in terms of bump inductance; (ii) the avoidance of unnecessary manual trial and error overheads in PKG PDN design; and (iii) more accurate design-phase PKG PDN quality assessment. We validate accuracy of our predictive models on PKG designs from industry. Experimental results show that, across a testbed of 17 industry PKG designs, we can predict bump inductance with an average absolute percentage error of 21.2% or less, given only pinmap and technology information. We improve prediction accuracy to achieve an average absolute percentage error of 17.5% or less when layout information is considered.

References

[1]
P. A. Brennan, N. Raver and A. E. Ruehli, "Three-Dimensional Inductance Computations with Partial Element Equivalent Circuits", IBM J. Research and Development 23(6) 1979, pp. 661--668.
[2]
J. H. Friedman, "Multivariate Adaptive Regression Splines", The Annals of Statistics 19(1) (1991), pp. 1--67.
[3]
S. S. Han, A. B. Kahng, S. Nath and A. Vydyanathan, "A Deep Learning Methodology to Proliferate Golden Signoff Timing", Proc. DATE, 2014, pp. 1--6.
[4]
T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.
[5]
A. B. Kahng, "Enhanced Metamodeling Techniques for High-Dimensional IC Design Estimation Problems", Proc. DATE, 2013, pp. 1861--1866.
[6]
C.-T. Tsai, "Package Inductance Characterization at High Frequencies", IEEE Trans. on CPMT 17(2) (1994), pp. 225--229.
[7]
C.-T. Tsai and W.-Y. Yip, "An Experimental Technique for Full Package Inductance Matrix Characterization", IEEE Trans. on CPMT 19(2) (1996), pp. 338--343.
[8]
Y. Shi and L. He, "Modeling and Design for Beyond-the-Die Power Integrity", Proc. ICCAD, 2010, pp. 411--416.
[9]
Gary Smith EDA. https://www.garysmitheda.com
[10]
T. Mandic, B. K. J. C. Nauwelaers and A. Baric, "Simple and Scalable Methodology for Equivalent Circuit Modeling of IC Packages", IEEE Trans. on CPMT 4(2) (2014), pp. 303--315.
[11]
M. Swaminathan and E. Engin, Power Integrity Modeling and Design for Semiconductors and Systems, Pearson Education, 2007.
[12]
{(Package team engineer), (foundry)}, personal communication, Mar. 2018.
[13]
Ansys. https://www.ansys.com
[14]
Applied Simulation Technology. www.apsimtech.com
[15]
Cadence Design Systems. https://www.cadence.com
[16]
JMP User Guide, http://www.jmp.com
[17]
Mentor, A Siemens Business. https://www.mentor.com
[18]
Py-earth, https://github.com/scikit-learn-contri/py-earth
[19]
Python3, https://www.python.org
[20]
SVM, scikit-learn.org/stable/modules/svm.html

Cited By

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  • (2024)Unlocking the Power of Machine Learning for Faster PCB Package and Board PDN Convergence2024 37th International Conference on VLSI Design and 2024 23rd International Conference on Embedded Systems (VLSID)10.1109/VLSID60093.2024.00054(287-292)Online publication date: 6-Jan-2024
  • (2023)Chip design with machine learning: a survey from algorithm perspectiveScience China Information Sciences10.1007/s11432-022-3772-866:11Online publication date: 19-Oct-2023
  • (2022)MLCAD: A Survey of Research in Machine Learning for CAD Keynote PaperIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.312476241:10(3162-3181)Online publication date: Oct-2022
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cover image ACM Conferences
ASPDAC '19: Proceedings of the 24th Asia and South Pacific Design Automation Conference
January 2019
794 pages
ISBN:9781450360074
DOI:10.1145/3287624
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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  • IEICE ESS: Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
  • IEEE CAS
  • IEEE CEDA
  • IPSJ SIG-SLDM: Information Processing Society of Japan, SIG System LSI Design Methodology

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Association for Computing Machinery

New York, NY, United States

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Published: 21 January 2019

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Cited By

View all
  • (2024)Unlocking the Power of Machine Learning for Faster PCB Package and Board PDN Convergence2024 37th International Conference on VLSI Design and 2024 23rd International Conference on Embedded Systems (VLSID)10.1109/VLSID60093.2024.00054(287-292)Online publication date: 6-Jan-2024
  • (2023)Chip design with machine learning: a survey from algorithm perspectiveScience China Information Sciences10.1007/s11432-022-3772-866:11Online publication date: 19-Oct-2023
  • (2022)MLCAD: A Survey of Research in Machine Learning for CAD Keynote PaperIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.312476241:10(3162-3181)Online publication date: Oct-2022
  • (2021)Machine Learning for Electronic Design Automation: A SurveyACM Transactions on Design Automation of Electronic Systems10.1145/345117926:5(1-46)Online publication date: 5-Jun-2021
  • (2020)PowerPlanningDL: Reliability-Aware Framework for On-Chip Power Grid Design using Deep Learning2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE48585.2020.9116536(1520-1525)Online publication date: Mar-2020
  • (2020)Adversarial Perturbation Attacks on ML-based CADACM Transactions on Design Automation of Electronic Systems10.1145/340828825:5(1-31)Online publication date: 21-Aug-2020
  • (2020)Surrogate-Based Analysis and Design Optimization of Power Delivery NetworksIEEE Transactions on Electromagnetic Compatibility10.1109/TEMC.2020.297394662:6(2528-2537)Online publication date: Dec-2020
  • (2020)Machine Learning Framework for Power Delivery Network Modelling2020 IEEE International Symposium on Electromagnetic Compatibility & Signal/Power Integrity (EMCSI)10.1109/EMCSI38923.2020.9191530(10-15)Online publication date: Jul-2020
  • (2019)GeniusRoute: A New Analog Routing Paradigm Using Generative Neural Network Guidance2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)10.1109/ICCAD45719.2019.8942164(1-8)Online publication date: Nov-2019
  • (2019)IncPIRD: Fast Learning-Based Prediction of Incremental IR Drop2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)10.1109/ICCAD45719.2019.8942110(1-8)Online publication date: Nov-2019

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