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Analog IC Placement Generation via Neural Networks from Unlabeled Data

In this book, innovative research using artificial neural networks (ANNs) is conducted to automate the placement task in analog integrated circuit layout design, by creating a generalized model that can generate valid layouts at push-button speed. Further, it exploits ANNs’ generalization and push-b...

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Main Authors: Gusmão, António. (Author, http://id.loc.gov/vocabulary/relators/aut), Horta, Nuno. (http://id.loc.gov/vocabulary/relators/aut), Lourenço, Nuno. (http://id.loc.gov/vocabulary/relators/aut), Martins, Ricardo. (http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2020.
Edition:1st ed. 2020.
Series:SpringerBriefs in Applied Sciences and Technology,
Subjects:
Machine learning.
Machine Learning.
Online Access:https://doi.org/10.1007/978-3-030-50061-0
Table of Contents:
  • Introduction
  • Related Work: Machine Learning and Electronic Design Automation
  • Unlabeled Data and Artificial Neural Networks
  • Placement Loss: Placement Constraints Description and Satisfiability Evaluation
  • Experimental Results in Industrial Case Studies
  • Conclusions. .

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