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Machine learning in VLSI computer-ai...

Elfadel, Ibrahim (Abe) M.

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Machine learning in VLSI computer-aided design

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning in VLSI computer-aided design/ edited by Ibrahim (Abe) M. Elfadel, Duane S. Boning, Xin Li.
other author:	Elfadel, Ibrahim (Abe) M.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xxii, 694 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter1: A Preliminary Taxonomy for Machine Learning in VLSI CAD -- Chapter2: Machine Learning for Compact Lithographic Process Models -- Chapter3: Machine Learning for Mask Synthesis -- Chapter4: Machine Learning in Physical Verification, Mask Synthesis, and Physical Design -- Chapter5: Gaussian Process-Based Wafer-Level Correlation Modeling and its Applications -- Chapter6: Machine Learning Approaches for IC Manufacturing Yield Enhancement -- Chapter7: Efficient Process Variation Characterization by Virtual Probe -- Chapter8: Machine learning for VLSI chip testing and semiconductor manufacturing process monitoring and improvement -- Chapter9: Machine Learning based Aging Analysis -- Chapter10: Extreme Statistics in Memories -- Chapter11: Fast Statistical Analysis Using Machine Learning -- Chapter12: Fast Statistical Analysis of Rare Circuit Failure Events -- Chapter13: Learning from Limited Data in VLSI CAD -- Chapter14: Large-Scale Circuit Performance Modeling by Bayesian Model Fusion -- Chapter15: Sparse Relevance Kernel Machine Based Performance Dependency Analysis of Analog and Mixed-Signal Circuits -- Chapter16: SiLVR: Projection Pursuit for Response Surface Modeling -- Chapter17: Machine Learning based System Optimization and Uncertainty Quantification of Integrated Systems -- Chapter18: SynTunSys: A Synthesis Parameter Autotuning System for Optimizing High-Performance Processors -- Chapter19: Multicore Power and Thermal Proxies Using Least-Angle -- Chapter20: A Comparative Study of Assertion Mining Algorithms in GoldMine -- Chapter21: Energy-Efficient Design of Advanced Machine Learning Hardware.
Contained By:	Springer eBooks
Subject:	Integrated circuits - Very large scale integration -
Online resource:	https://doi.org/10.1007/978-3-030-04666-8
ISBN:	9783030046668

Machine learning in VLSI computer-aided design
Machine learning in VLSI computer-aided design[electronic resource] /edited by Ibrahim (Abe) M. Elfadel, Duane S. Boning, Xin Li. - Cham :Springer International Publishing :2019. - xxii, 694 p. :ill., digital ;24 cm.

Chapter1: A Preliminary Taxonomy for Machine Learning in VLSI CAD -- Chapter2: Machine Learning for Compact Lithographic Process Models -- Chapter3: Machine Learning for Mask Synthesis -- Chapter4: Machine Learning in Physical Verification, Mask Synthesis, and Physical Design -- Chapter5: Gaussian Process-Based Wafer-Level Correlation Modeling and its Applications -- Chapter6: Machine Learning Approaches for IC Manufacturing Yield Enhancement -- Chapter7: Efficient Process Variation Characterization by Virtual Probe -- Chapter8: Machine learning for VLSI chip testing and semiconductor manufacturing process monitoring and improvement -- Chapter9: Machine Learning based Aging Analysis -- Chapter10: Extreme Statistics in Memories -- Chapter11: Fast Statistical Analysis Using Machine Learning -- Chapter12: Fast Statistical Analysis of Rare Circuit Failure Events -- Chapter13: Learning from Limited Data in VLSI CAD -- Chapter14: Large-Scale Circuit Performance Modeling by Bayesian Model Fusion -- Chapter15: Sparse Relevance Kernel Machine Based Performance Dependency Analysis of Analog and Mixed-Signal Circuits -- Chapter16: SiLVR: Projection Pursuit for Response Surface Modeling -- Chapter17: Machine Learning based System Optimization and Uncertainty Quantification of Integrated Systems -- Chapter18: SynTunSys: A Synthesis Parameter Autotuning System for Optimizing High-Performance Processors -- Chapter19: Multicore Power and Thermal Proxies Using Least-Angle -- Chapter20: A Comparative Study of Assertion Mining Algorithms in GoldMine -- Chapter21: Energy-Efficient Design of Advanced Machine Learning Hardware.

ISBN: 9783030046668

Standard No.: 10.1007/978-3-030-04666-8doiSubjects--Topical Terms:

715562
Integrated circuits
--Very large scale integration

LC Class. No.: TK7874.75

Dewey Class. No.: 621.395

Machine learning in VLSI computer-aided design
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245 0 0 $a Machine learning in VLSI computer-aided design $h [electronic resource] / $c edited by Ibrahim (Abe) M. Elfadel, Duane S. Boning, Xin Li.
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505 0 $a Chapter1: A Preliminary Taxonomy for Machine Learning in VLSI CAD -- Chapter2: Machine Learning for Compact Lithographic Process Models -- Chapter3: Machine Learning for Mask Synthesis -- Chapter4: Machine Learning in Physical Verification, Mask Synthesis, and Physical Design -- Chapter5: Gaussian Process-Based Wafer-Level Correlation Modeling and its Applications -- Chapter6: Machine Learning Approaches for IC Manufacturing Yield Enhancement -- Chapter7: Efficient Process Variation Characterization by Virtual Probe -- Chapter8: Machine learning for VLSI chip testing and semiconductor manufacturing process monitoring and improvement -- Chapter9: Machine Learning based Aging Analysis -- Chapter10: Extreme Statistics in Memories -- Chapter11: Fast Statistical Analysis Using Machine Learning -- Chapter12: Fast Statistical Analysis of Rare Circuit Failure Events -- Chapter13: Learning from Limited Data in VLSI CAD -- Chapter14: Large-Scale Circuit Performance Modeling by Bayesian Model Fusion -- Chapter15: Sparse Relevance Kernel Machine Based Performance Dependency Analysis of Analog and Mixed-Signal Circuits -- Chapter16: SiLVR: Projection Pursuit for Response Surface Modeling -- Chapter17: Machine Learning based System Optimization and Uncertainty Quantification of Integrated Systems -- Chapter18: SynTunSys: A Synthesis Parameter Autotuning System for Optimizing High-Performance Processors -- Chapter19: Multicore Power and Thermal Proxies Using Least-Angle -- Chapter20: A Comparative Study of Assertion Mining Algorithms in GoldMine -- Chapter21: Energy-Efficient Design of Advanced Machine Learning Hardware.
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