| 书目名称 | Machine Learning in VLSI Computer-Aided Design | | 编辑 | Ibrahim (Abe) M. Elfadel,Duane S. Boning,Xin Li | | 视频video | | | 概述 | 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 learn | | 图书封面 |  | | 描述 | .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 se | | 出版日期 | Book 2019 | | 关键词 | VLSI Design; VLSI Verification; VLSI Testing; VLSI Analog Circuits; CMOS VLSI Design | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-04666-8 | | isbn_ebook | 978-3-030-04666-8 | | copyright | Springer Nature Switzerland AG 2019 |
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发表于 2025-3-21 17:04:08
