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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
Attention for Chapter 1: A Preliminary Taxonomy for Machine Learning in VLSI CAD
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Citations

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Chapter title
A Preliminary Taxonomy for Machine Learning in VLSI CAD
Chapter number 1
Book title
Machine Learning in VLSI Computer-Aided Design
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-030-04666-8_1
Book ISBNs
978-3-03-004665-1, 978-3-03-004666-8
Authors

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

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 50%
Student > Postgraduate 1 13%
Other 1 13%
Student > Master 1 13%
Unknown 1 13%
Readers by discipline Count As %
Computer Science 4 50%
Engineering 2 25%
Unknown 2 25%

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