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Machine Learning in VLSI Computer-Aided Design

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Cover of 'Machine Learning in VLSI Computer-Aided Design'

Table of Contents

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    Book Overview
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    Chapter 1 A Preliminary Taxonomy for Machine Learning in VLSI CAD
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    Chapter 2 Machine Learning for Compact Lithographic Process Models
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    Chapter 3 Machine Learning for Mask Synthesis
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    Chapter 4 Machine Learning in Physical Verification, Mask Synthesis, and Physical Design
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    Chapter 5 Gaussian Process-Based Wafer-Level Correlation Modeling and Its Applications
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    Chapter 6 Machine Learning Approaches for IC Manufacturing Yield Enhancement
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    Chapter 7 Efficient Process Variation Characterization by Virtual Probe
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    Chapter 8 Machine Learning for VLSI Chip Testing and Semiconductor Manufacturing Process Monitoring and Improvement
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    Chapter 9 Machine Learning-Based Aging Analysis
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    Chapter 10 Extreme Statistics in Memories
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    Chapter 11 Fast Statistical Analysis Using Machine Learning
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    Chapter 12 Fast Statistical Analysis of Rare Circuit Failure Events
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    Chapter 13 Learning from Limited Data in VLSI CAD
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    Chapter 14 Large-Scale Circuit Performance Modeling by Bayesian Model Fusion
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    Chapter 15 Sparse Relevance Kernel Machine-Based Performance Dependency Analysis of Analog and Mixed-Signal Circuits
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    Chapter 16 SiLVR: Projection Pursuit for Response Surface Modeling
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    Chapter 17 Machine Learning-Based System Optimization and Uncertainty Quantification for Integrated Systems
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    Chapter 18 SynTunSys: A Synthesis Parameter Autotuning System for Optimizing High-Performance Processors
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    Chapter 19 Multicore Power and Thermal Proxies Using Least-Angle Regression
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    Chapter 20 A Comparative Study of Assertion Mining Algorithms in GoldMine
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    Chapter 21 Energy-Efficient Design of Advanced Machine Learning Hardware
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Citations

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Title
Machine Learning in VLSI Computer-Aided Design
Published by
Springer International Publishing, April 2019
DOI 10.1007/978-3-030-04666-8
ISBNs
978-3-03-004665-1, 978-3-03-004666-8
Editors

Elfadel, Ibrahim (Abe) M., Boning, Duane S., Li, Xin

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 61 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 20%
Student > Ph. D. Student 11 18%
Student > Bachelor 7 11%
Researcher 5 8%
Student > Doctoral Student 3 5%
Other 6 10%
Unknown 17 28%
Readers by discipline Count As %
Engineering 21 34%
Computer Science 16 26%
Physics and Astronomy 3 5%
Arts and Humanities 1 2%
Economics, Econometrics and Finance 1 2%
Other 3 5%
Unknown 16 26%