Overview
- Describes the advances achieved in the field of machine learning and electronic design automation for analog IC
- Presents innovative research on the use of artificial neural networks (ANNs)
- Details the optimal description of the input/output data relation
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
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Table of contents (6 chapters)
Keywords
About this book
In the experimental results chapter, the trained ANNs are used to produce a variety of valid placement solutions even beyond the scope of the training/validation sets, demonstrating the model’s effectiveness in terms of identifying common components between newer topologies and reutilizing the acquired knowledge. Lastly, the methodology used can readily adapt to the given problem’s context (high label production cost), resulting in an efficient, inexpensive and fast model.
Authors and Affiliations
Bibliographic Information
Book Title: Analog IC Placement Generation via Neural Networks from Unlabeled Data
Authors: António Gusmão, Nuno Horta, Nuno Lourenço, Ricardo Martins
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-3-030-50061-0
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-030-50060-3Published: 01 July 2020
eBook ISBN: 978-3-030-50061-0Published: 30 June 2020
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: XIII, 87
Number of Illustrations: 29 b/w illustrations, 39 illustrations in colour
Topics: Machine Learning