Can I read Machine Learning in VLSI Computer-Aided Design on EtoBox?

Machine Learning in VLSI Computer-Aided Design by Ibrahim (Abe) M. Elfadel (editor), Duane S. Boning (editor), Xin Li (editor) is a nonfiction available to read on EtoBox.

What is Machine Learning in VLSI Computer-Aided Design about?

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 lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. * Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; * Discusses the use of machine learning techniques in the context of analog and digital synthesis; * Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; * Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the

Who reads Machine Learning in VLSI Computer-Aided Design?

Author

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

Publisher

Springer International Publishing : Imprint: Springer

Published

2019

Language

EN

ISBN

9783030046668

Category

nonfiction

Subjects

Computer Science, Engineering, Stem