SpringerBriefs in Applied Sciences and Technology

Analog IC Placement Generation via Neural Networks from Unlabeled Data

Authors: Gusmão, A., Horta, N., Lourenço, N., VAT PT502854200, I. de T.

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  • 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
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eBook $44.99
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  • ISBN 978-3-030-50061-0
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Softcover $59.99
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  • ISBN 978-3-030-50060-3
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About this book

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-button speed prediction (once fully trained) capabilities, and details the optimal description of the input/output data relation. The description developed here is chiefly reflected in two of the system’s characteristics: the shape of the input data and the minimized loss function. In order to address the latter, abstract and segmented descriptions of both the input data and the objective behavior are developed, which allow the model to identify, in newer scenarios, sub-blocks which can be found in the input data. This approach yields device-level descriptions of the input topology that, for each device, focus on describing its relation to every other device in the topology. By means of these descriptions, an unfamiliar overall topology can be broken down into devices that are subject to the same constraints as a device in one of the training topologies.

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.                           

Table of contents (6 chapters)

Table of contents (6 chapters)

Buy this book

eBook $44.99
price for USA in USD
  • ISBN 978-3-030-50061-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $59.99
price for USA in USD
  • ISBN 978-3-030-50060-3
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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Bibliographic Information

Bibliographic Information
Book Title
Analog IC Placement Generation via Neural Networks from Unlabeled Data
Authors
Series Title
SpringerBriefs in Applied Sciences and Technology
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
The Author(s), under exclusive license to Springer Nature Switzerland AG
eBook ISBN
978-3-030-50061-0
DOI
10.1007/978-3-030-50061-0
Softcover ISBN
978-3-030-50060-3
Series ISSN
2191-530X
Edition Number
1
Number of Pages
XIII, 87
Number of Illustrations
29 b/w illustrations, 39 illustrations in colour
Topics