Machine Learning in VLSI Computer-Aided Design

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Elfadel, Ibrahim (Abe) M. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Boning, Duane S. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Li, Xin (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Θέματα: Electronic circuits.; Microprocessors.; Logic design.; Circuits and Systems.; Processor Architectures.; Logic 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...

Πίνακας περιεχομένων:

• 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.