IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-13074-8_5.html
   My bibliography  Save this book chapter

Deep Learning for Analyzing Power Delivery Networks and Thermal Networks

In: Machine Learning Applications in Electronic Design Automation

Author

Listed:
  • Vidya A. Chhabria

    (University of Minnesota)

  • Sachin S. Sapatnekar

    (University of Minnesota)

Abstract

The design of on-chip power delivery networks (PDNs) and thermal networks involves the solution of large systems of linear equations. This computational intensive step is a critical part of the IC design process and has been a significant computational bottleneck for electronic design automation. Machine learning techniques can efficiently solve these problems by performing fast and accurate analysis and optimization. This chapter presents ML methods in this domain: for analyzing PDNs for IR drop and electromigration, for analyzing thermal networks for temperature, for optimizing PDNs by mapping the problem to a classification problem, and for generating PDN benchmarks.

Suggested Citation

  • Vidya A. Chhabria & Sachin S. Sapatnekar, 2022. "Deep Learning for Analyzing Power Delivery Networks and Thermal Networks," Springer Books, in: Haoxing Ren & Jiang Hu (ed.), Machine Learning Applications in Electronic Design Automation, chapter 0, pages 115-150, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-13074-8_5
    DOI: 10.1007/978-3-031-13074-8_5
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-031-13074-8_5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.