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- Author: Rohit Sharma
- Publisher: INDEPENDENTLY PUBLISHED
- Year: 2018
- Pages: 221
- ISBN-10: 1980554358
- ISBN-13: 9781980554356
- Format: 15.2 x 22.9 x 1.3 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
This book presents a hands-on approach for solving electronic design automation problems with modern machine intelligence techniques by including step-by-step development of commercial grade design applications including resistance estimation, capacitance estimation, cell classification and others using dataset extracted from designs at 20nm. It walks the reader step by step in building solution flow for EDA problems with Python and Tensorflow.Intended audience includes design automation engineers, managers, executives, research professionals, graduate students, Machine learning enthusiasts, EDA and CAD developers, mentors, and the merely inquisitive. It is organized to serve as a compendium to a beginner, a ready reference to intermediate and source for an expert.
EXTRA 10 % discount with code: EXTRA
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- Author: Rohit Sharma
- Publisher: INDEPENDENTLY PUBLISHED
- Year: 2018
- Pages: 221
- ISBN-10: 1980554358
- ISBN-13: 9781980554356
- Format: 15.2 x 22.9 x 1.3 cm, minkšti viršeliai
- Language: English English
This book presents a hands-on approach for solving electronic design automation problems with modern machine intelligence techniques by including step-by-step development of commercial grade design applications including resistance estimation, capacitance estimation, cell classification and others using dataset extracted from designs at 20nm. It walks the reader step by step in building solution flow for EDA problems with Python and Tensorflow.Intended audience includes design automation engineers, managers, executives, research professionals, graduate students, Machine learning enthusiasts, EDA and CAD developers, mentors, and the merely inquisitive. It is organized to serve as a compendium to a beginner, a ready reference to intermediate and source for an expert.
Reviews