research-article

Data Mining In EDA - Basic Principles, Promises, and Constraints

Authors:

Magdy S. AbadirAuthors Info & Claims

DAC '14: Proceedings of the 51st Annual Design Automation Conference

Pages 1 - 6

https://doi.org/10.1145/2593069.2596675

Published: 01 June 2014 Publication History

Abstract

This paper discusses the basic principles of applying data mining in Electronic Design Automation. It begins by introducing several important concepts in statistical learning and summarizes different types of learning algorithms. Then, the experience of developing a practical data mining application is described, including promises that are demonstrated through positive results based on industrial settings and constraints explained in their respective application contexts.

References

[1]

Michael J. Kearns and Umesh V. Vazirani. An Introduction to Computational Learning Theory, MIT Press, 1994.

Digital Library

[2]

L. G. Valiant. A theory of learnable. Communications of ACM, 27 (11), pp. 1134--1142, 1984.

Digital Library

[3]

Onur Guzey, et. al. Extracting a Simplified View of Design Functionality Based on Vector Simulation. Lecture Note in Computer Science, LNCS, Vol 4383, 2007, pp. 34--49.

Digital Library

[4]

V. Vapnik, The nature of Statistical Learning Theory. 2nd ed., Springer, 1999.

Digital Library

[5]

David R. Hardoon, Sandor Szedmak, John Shawe-Taylor. Canonical correlation analysis; An overview with application to learning methods Neural Computation, 16 (12), pp. 2639--2664, 2004.

Digital Library

[6]

Trevor Hastie, et al. The Elements of Statistical Learning - Date Mining, Inference, and Prediction. Springer Series in Statistics, 2001

[7]

Leo Preiman, et al. Classification and Regression Trees. Wadsworth, 1984.

[8]

Leo Breiman, Random Forests Machine Learning Journal (45), 2001, pp. 5--32.

Digital Library

[9]

N. Lavrač, B. Kavšek, P. Flach, and L. Todorovski. Rule induction for subgroup discovery with CN2-SD Journal of Machine Learning Research, 5:153--188, Dec. 2004.

Digital Library

[10]

David MacKay, Information Theory, Inference, and Learning Algorithms. Cambridge Univ. Press, 2003.

Digital Library

[11]

Bernhard Schölkopf, and Alexander J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press, 2001.

Digital Library

[12]

J. Shawe-Taylor, N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge University Press 2004.

Digital Library

[13]

Dragoljub (Gagi) Drmanac, Frank Liu, Li-C. Wang. Predicting Variability in Nanoscale Lithography Processes. ACM/IEEE DAC, 2009, pp. 545--550.

Digital Library

[14]

Wen Chen, et. al. Novel Test Detection to Improve Simulation Efficiency A Commercial Experiment. ACM/IEEE ICCAD, 2012.

Digital Library

[15]

G. Batista. A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data. Sigkdd Explorations, 6(1), pp. 20--29, 2004.

Digital Library

[16]

Nik Sumikawa, et. al. Screening Customer Returns With Multivariate Test Analysis. IEEE ITC, 2012.

Digital Library

[17]

Nik Sumikawa, et. al. Important Test Selection For Screening Potential Customer Returns. IEEE VLSI Design Automation and Test Symposium, 2011, pp. 171--174.

[18]

Z. Zheng, X. Wu, and R. Srihari. Feature Selection for Text Categorization on Imbalanced Data. Sigkdd Explorations, 6 (1), pp. 80--89, 2004.

Digital Library

[19]

C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. MIT Press, 2006.

Digital Library

[20]

Janine Chen, et al. Data learning techniques and methodology for Fmax prediction. IEEE ITC, 2009.

[21]

http://scikit-learn.org/stable/modules/clustering.html

[22]

I.T. Jolliffe, Principal Component Analysis. Springer, 1986.

[23]

A. Hyvarinen, et. al. Independent Component Analysis. Wiley Series on Adaptive and Learning Systems, 2001

[24]

Peter M. O'Neill. Production Multivariate Outlier Detection Using Principal Components. IEEE International Test Conference, 2008.

[25]

Ritesh Turakhia, et al. Defect Screening Using Independent Component Analysis on IDDQ. IEEE VLSI Test Symposium, 2005, pp. 427--432.

Digital Library

[26]

Chengqi Zhang and Shichao Zhang. Association Rule Mining, Models and Algorithms. Lecture Notes in Computer Science Vol. 2307, Springer 2002.

Digital Library

[27]

Onur Guzey, et al. Functional test selection based on unsupervised support vector analysis. ACM/IEEE Design Automation Conference, 2008, pp. 262--267.

Digital Library

[28]

Wen Chen, et al., Simulation knowledge extraction and reuse in constrained random processor verification. In ACM/IEEE Design Automation Conference, 2013.

Digital Library

[29]

Li-C. Wang, Pouria Bastani, Magdy S. Abadir. Design-silicon timing correlation --- a data mining perspective. In ACM/IEEE DAC 2007, pp. 384--389.

Digital Library

[30]

P. Bastani, et. al. Statistical Diagnosis of Unmodeled Timing Effect. ACM/IEEE DAC, 2008, pp. 355--360.

Digital Library

[31]

Janine Chen, et. al. Mining AC Delay Measurements for Understanding Speed-limiting Paths. IEEE ITC, 2010.

[32]

Nik Sumikawa, et al. A Pattern Mining Framework for Inter-Wafer Abnormality Analysis. IEEE ITC, 2013

[33]

Dragoljub (Gagi) Drmanac, et al., Wafer Probe Test Cost Reduction of an RF/A Device by Automatic Testset Minimization: A Case Study. IEEE ITC, 2011.

[34]

Krzysztof J. Cios, et. al., Data Mining - A Knowledge Discovery Approach, Springer, 2007.

Digital Library

Cited By

Baig IFarooq U(2022)Efficient Detailed Routing for FPGA Back-End Flow Using Reinforcement LearningElectronics10.3390/electronics1114224011:14(2240)Online publication date: 18-Jul-2022
https://doi.org/10.3390/electronics11142240
Cui WTzimpragos GTao YMcmahan JDangwal DTsiskaridze NMichelogiannakis GVasudevan DSherwood T(2019)Language Support for Navigating Architecture Design in Closed FormACM Journal on Emerging Technologies in Computing Systems10.1145/336004716:1(1-28)Online publication date: 25-Oct-2019
https://dl.acm.org/doi/10.1145/3360047
Smith KThornton M(2019)Higher Dimension Quantum Entanglement GeneratorsACM Journal on Emerging Technologies in Computing Systems10.1145/334550116:1(1-21)Online publication date: 15-Oct-2019
https://dl.acm.org/doi/10.1145/3345501
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Index Terms

Data Mining In EDA - Basic Principles, Promises, and Constraints
1. Hardware
  1. Integrated circuits
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Other conferences

DAC '14: Proceedings of the 51st Annual Design Automation Conference

June 2014

1249 pages

ISBN:9781450327305

DOI:10.1145/2593069

Copyright © 2014 ACM.

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]

In-Cooperation

EDAC: Electronic Design Automation Consortium
SIGBED: ACM Special Interest Group on Embedded Systems
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CEDA

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Association for Computing Machinery

New York, NY, United States

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Published: 01 June 2014

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DAC '14

DAC '14: The 51st Annual Design Automation Conference 2014

June 1 - 5, 2014

CA, San Francisco, USA

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

Baig IFarooq U(2022)Efficient Detailed Routing for FPGA Back-End Flow Using Reinforcement LearningElectronics10.3390/electronics1114224011:14(2240)Online publication date: 18-Jul-2022
https://doi.org/10.3390/electronics11142240
Cui WTzimpragos GTao YMcmahan JDangwal DTsiskaridze NMichelogiannakis GVasudevan DSherwood T(2019)Language Support for Navigating Architecture Design in Closed FormACM Journal on Emerging Technologies in Computing Systems10.1145/336004716:1(1-28)Online publication date: 25-Oct-2019
https://dl.acm.org/doi/10.1145/3360047
Smith KThornton M(2019)Higher Dimension Quantum Entanglement GeneratorsACM Journal on Emerging Technologies in Computing Systems10.1145/334550116:1(1-21)Online publication date: 15-Oct-2019
https://dl.acm.org/doi/10.1145/3345501
Bonna RLoubach DUngureanu GSander I(2019)Modeling and Simulation of Dynamic Applications Using Scenario-Aware DataflowACM Transactions on Design Automation of Electronic Systems10.1145/334299724:5(1-29)Online publication date: 21-Aug-2019
https://dl.acm.org/doi/10.1145/3342997
Raval RBadii A(2019)Investigating the Impact of Image Content on the Energy Efficiency of Hardware-accelerated Digital Spatial FiltersACM Transactions on Design Automation of Electronic Systems10.1145/334181924:5(1-34)Online publication date: 17-Oct-2019
https://dl.acm.org/doi/10.1145/3341819
Li XMaskell D(2019)Time-Multiplexed FPGA Overlay ArchitecturesACM Transactions on Design Automation of Electronic Systems10.1145/333986124:5(1-19)Online publication date: 23-Jul-2019
https://dl.acm.org/doi/10.1145/3339861
Kang WWu BChen XZhu DWang ZZhang XZhou YZhang YZhao W(2019)A Comparative Cross-layer Study on Racetrack MemoriesACM Journal on Emerging Technologies in Computing Systems10.1145/333333616:1(1-17)Online publication date: 3-Oct-2019
https://dl.acm.org/doi/10.1145/3333336
Cakir BMalik S(2019)Revealing Cluster Hierarchy in Gate-level ICs Using Block Diagrams and Cluster Estimates of Circuit EmbeddingsACM Transactions on Design Automation of Electronic Systems10.1145/332908124:5(1-19)Online publication date: 12-Jun-2019
https://dl.acm.org/doi/10.1145/3329081
Wang HHe K(2019)Improving Test and Diagnosis Efficiency through Ensemble Reduction and LearningACM Transactions on Design Automation of Electronic Systems10.1145/332875424:5(1-26)Online publication date: 5-Jun-2019
https://dl.acm.org/doi/10.1145/3328754
Vegesna SNara ASk N(2019)A Novel Rule Mapping on TCAM for Power Efficient Packet ClassificationACM Transactions on Design Automation of Electronic Systems10.1145/332810324:5(1-23)Online publication date: 7-Jun-2019
https://dl.acm.org/doi/10.1145/3328103
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