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Published in:
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2019 | OriginalPaper | Chapter

1. A Preliminary Taxonomy for Machine Learning in VLSI CAD

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

Published in: Machine Learning in VLSI Computer-Aided Design

Publisher: Springer International Publishing

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Abstract

Machine learning is transforming many industries and areas of work, and the design of very large-scale integrated (VLSI) circuits and systems is no exception. The purpose of this book is to bring to the interested reader a cross-section of the connections between existing and emerging machine learning methods and VLSI computer aided design (CAD). In this brief introduction, we begin with a high-level taxonomy of machine learning methods. We then turn to the design abstraction hierarchy in VLSI CAD, and note the needs and challenges in design where machine learning methods can be applied to extend the capabilities of existing VLSI CAD tools and methodologies. Finally, we outline the organization of this book, highlighting the range of machine learning methods that each of the chapters contributed to this book build on.

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Metadata
Title
A Preliminary Taxonomy for Machine Learning in VLSI CAD
Authors
Duane S. Boning
Ibrahim (Abe) M. Elfadel
Xin Li
Copyright Year
2019
Book
Machine Learning in VLSI Computer-Aided Design

Print ISBN: 978-3-030-04665-1

Electronic ISBN: 978-3-030-04666-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-04666-8_1