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Published in:
A Preliminary Taxonomy for Machine Learning in VLSI CAD Read chapter Read first chapter

2019 | OriginalPaper | Chapter

13. Learning from Limited Data in VLSI CAD

Author : Li-C. Wang

Published in: Machine Learning in VLSI Computer-Aided Design

Publisher: Springer International Publishing

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Abstract

Applying machine learning to analyze data from design and test flows has received growing interests in recent years. In some applications, data can be limited and the core of analytics becomes a feature search problem. In this context, the chapter explains the challenges for adopting a traditional machine learning problem formulation view. An adjusted machine learning view is suggested where learning from limited data is treated as an iterative feature search process. The theoretical and practical considerations for implementing such a search process are discussed.

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previous chapter Fast Statistical Analysis of Rare Circuit Failure Events
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Literature
1.
L.-C. Wang, Experience of data analytics in EDA and test - principles, promises, and challenges. IEEE Trans. CAD 36(6), 885–898 (2017)CrossRef
2.
W. Chen, L.-C. Wang, J. Bhadra, Simulation knowledge extraction and reuse in constrained random processor verification, in ACM/IEEE Design Automation Conference (2013)
3.
L.-C. Wang, Data mining in functional test content optimization, in ACM/IEEE Asian South Pacific Design Automation Conference (2015)
4.
G. Drmanac, F. Liu, L.-C. Wang, Predicting variability in nanoscale lithography processes, in ACM/IEEE Design Automation Conference (2009)
5.
J. Chen, B. Bolin, L.-C. Wang, J. Zeng, D. (Gagi) Drmanac, M. Mateja, Mining AC delay measurements for understanding speed-limiting paths, in IEEE International Test Conference (2010)
6.
I. Goodfellow, Y. Benjio, A. Courville, Deep Learning (The MIT Press, Cambridge, 2016)MATH
7.
J. Chen, L.-C. Wang, P.-H. Chang, J. Zeng, S. Yu, M. Metaja, Data learning techniques and methodology for Fmax prediction, in IEEE International Test Conference (2009)
8.
J. Tikkanen, S. Siatkowski, N. Sumikawa, L.-C. Wang, M.S. Abadir, Yield optimization using advanced statistical correlation methods, in IEEE International Test Conference (2014)
9.
S. Siatkowski, L.-C. Wang, N. Sumikawa, L. Winemberg, Learning the process for correlation analysis, in IEEE VLSI Test Symposium (2017)
10.
F. Pedregosa et al., Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2010)MathSciNetMATH
11.
N. Callegari, D. (Gagi) Drmanac, L.-C. Wang, M.S. Abadir, Classification rule learning using subgroup discovery of cross-domain attributes responsible for design-silicon mismatch, in ACM/IEEE Design Automation Conference, 374–379 (2010)
12.
D.H. Wolpert, The lack of a priori distinctions between learning algorithms. Neural Comput. 8(7), 1341–1390 (1996)CrossRef
13.
M. Nero, J. Shan, L.-C. Wang, N. Sumikawa, Concept recognition in production yield data analytics, in IEEE International Test Conference (2018)
14.
K. Hsieh, L.-C. Wang, W. Chen, J. Bhadra, Learning to produce direct tests for security verification using constrained process discovery, in Design Automation Conference (2017)
15.
K-K. Hsieh, S. Siatkowski, L.-C. Wang, W. Chen, J. Bhadra, Feature extraction from design documents to enable rule learning for improving assertion coverage, in ACM/IEEE Asia South Pacific Design Automation Conference (2017)
16.
L.G. Valiant, A theory of learnable. Commun. ACM 27(11), 1134–1142 (1984)CrossRef
17.
M.J. Kearns, U. Vazirani, An Introduction to Computational Learning Theory (The MIT Press, Cambridge, 1994)CrossRef
18.
V. Vapnik, The Nature of Statistical Learning Theory (Springer, New York, 2000)CrossRef
19.
M.J. Kearns, U. Vazirani, Cryptographic limitations on learning Boolean formulae and finite automata. J. ACM 14(1), 67–95 (1994)MathSciNetCrossRef
20.
A. Daniely, N. Linial, S. Shaleve-Shwartz, From average case complexity to improper learning complexity, in ACM Symposium on Theory of Computing (2014), pp. 441–448.
21.
D.H. Wolpert, The relationship between Occam’s Razor and convergent guessing. Complex Syst. 4, 319–368 (1990)MathSciNetMATH
22.
J. Pearl, On the connection between the complexity and credibility of inferred models. Int. J. General Syst. 4, 255–264 (1978)MathSciNetCrossRef
23.
A. Daniely, S. Shalev-Shwartz, Complexity theoretic limitations on learning DNF’s, in PMLR: Proceeding of Machine Learning Research, vol. 49 (2016), pp. 815–830
24.
R. Motwani, P. Raghavani, Randomized Algorithms (Cambridge University Press, Cambridge, 1995)CrossRef
25.
D. Haussler, Quantifying inductive bias: AI learning algorithms and valiant’s learning framework. Artif. Intell. 36, 177–221 (1998)MathSciNetCrossRef
26.
Y. Ben-Haim, A. Ivrii, O. Margalit, A. Matsliah, Perfect hashing and CNF encodings of cardinality constraints, in International Conference on Theory and Applications of Satisfiability Testing (Springer, Berlin, 2012), pp. 397–409MATH
27.
C. Sinz, Towards an optimal CNF encoding of boolean cardinality constraints, in International Conference on Principles and Practice of Constraint Programming (Springer, Berlin, 2005), pp. 827–831MATH
28.
K. Hsieh, L.-C. Wang, A concept learning tool based on calculating version space cardinality (2018). arXiv:1803.08625v1
29.
A. Biere, Lingeling, Plingeling and Treengeling entering the SAT competition, in Proceedings of SAT Competition (2013)
30.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, J. Bengio, Generative adversarial networks (2014). arXiv:1406.2661
31.
A. Radford, L. Metz, S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks (2016). arXiv:1511.06434v2
Metadata
Title
Learning from Limited Data in VLSI CAD
Author
Li-C. Wang
Copyright Year
2019
Book
Machine Learning in VLSI Computer-Aided Design

Print ISBN: 978-3-030-04665-1

Electronic ISBN: 978-3-030-04666-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-04666-8_13