Machine Learning in VLSI Computer-Aided Design

This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lith...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Elfadel, Ibrahim (Abe) M. (Editor), Boning, Duane S. (Editor), Li, Xin (Editor)
Format: e-Book
Language:English
Published: Cham : Springer International Publishing : 2019.
Imprint: Springer,
Edition:1st ed. 2019.
Subjects:
Electronic circuits.
Microprocessors.
Computer architecture.
Logic design.
Electronic Circuits and Systems.
Processor Architectures.
Logic Design.
Online Access:Full-text access
Table of Contents:
  • 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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