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Analog IC Placement Generation via Neural Networks from Unlabeled Data

  • Book
  • © 2020

Overview

Authors:
  1. António Gusmão

    1. Instituto de Telecomunicações, Lisbon, Portugal

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  2. Nuno Horta

    1. Instituto Superior Técnico, Instituto de Telecomunicações, Lisbon, Portugal

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  3. Nuno Lourenço

    1. Instituto de Telecomunicações, Lisbon, Portugal

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  4. Ricardo Martins

    1. Instituto de Telecomunicações, Lisbon, Portugal

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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

Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Introduction

    • António Gusmão, Nuno Horta, Nuno Lourenço, Ricardo Martins
    Pages 1-5

  3. Artificial Neural Network Overview

    • António Gusmão, Nuno Horta, Nuno Lourenço, Ricardo Martins
    Pages 7-24

  4. State-of-the-Art in Analog Integrated Circuit Placement

    • António Gusmão, Nuno Horta, Nuno Lourenço, Ricardo Martins
    Pages 25-37

  5. ANN Models for Analog Placement Automation

    • António Gusmão, Nuno Horta, Nuno Lourenço, Ricardo Martins
    Pages 39-58

  6. Results

    • António Gusmão, Nuno Horta, Nuno Lourenço, Ricardo Martins
    Pages 59-81

  7. Conclusions and Future Work

    • António Gusmão, Nuno Horta, Nuno Lourenço, Ricardo Martins
    Pages 83-84

  8. Back Matter

    Pages 85-87
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Keywords

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 thesedescriptions, 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.                           

Authors and Affiliations

  • Instituto de Telecomunicações, Lisbon, Portugal

    António Gusmão, Nuno Lourenço, Ricardo Martins

  • Instituto Superior Técnico, Instituto de Telecomunicações, Lisbon, Portugal

    Nuno Horta

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

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