Deep Learning for Analyzing Power Delivery Networks and Thermal Networks

Vidya A. Chhabria () and Sachin S. Sapatnekar ()
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Vidya A. Chhabria: University of Minnesota
Sachin S. Sapatnekar: University of Minnesota

Chapter Chapter 5 in Machine Learning Applications in Electronic Design Automation, 2022, pp 115-150 from Springer

Abstract: 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.

Keywords: Power delivery network (PDN); IR drop analysis; Electromigration hotspot classification; Thermal analysis; Convolutional neural networks (CNN); U-Nets; Long short-term memories (LSTMs) (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-13074-8_5

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DOI: 10.1007/978-3-031-13074-8_5

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