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Machine Learning Applications in Physical Design: Recent Results and Directions

Published: 25 March 2018 Publication History

Abstract

In the late-CMOS era, semiconductor and electronics companies face severe product schedule and other competitive pressures. In this context, electronic design automation (EDA) must deliver "design-based equivalent scaling" to help continue essential industry trajectories. A powerful lever for this will be the use of machine learning techniques, both inside and "around" design tools and flows. This paper reviews opportunities for machine learning with a focus on IC physical implementation. Example applications include (1) removing unnecessary design and modeling margins through correlation mechanisms, (2) achieving faster design convergence through predictors of downstream flow outcomes that comprehend both tools and design instances, and (3) corollaries such as optimizing the usage of design resources licenses and available schedule. The paper concludes with open challenges for machine learning in IC physical design.

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cover image ACM Conferences
ISPD '18: Proceedings of the 2018 International Symposium on Physical Design
March 2018
178 pages
ISBN:9781450356268
DOI:10.1145/3177540
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 March 2018

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Author Tags

  1. machine learning
  2. physical design

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ISPD '18
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ISPD '18: International Symposium on Physical Design
March 25 - 28, 2018
California, Monterey, USA

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Overall Acceptance Rate 62 of 172 submissions, 36%

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  • (2025)Eh-DRVP: Combining placement and global routing data in a hyper-image-based DRV predictorIntegration10.1016/j.vlsi.2024.102309101(102309)Online publication date: Mar-2025
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  • (2024)AI-Driven DRC Routing Convergence in IC Design : A Paradigm Shift in Semiconductor DevelopmentInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT24105106410:5(744-755)Online publication date: 21-Oct-2024
  • (2024)Runtime Prediction for VLSI Physical Design Processes using Machine Learning2024 28th International Symposium on VLSI Design and Test (VDAT)10.1109/VDAT63601.2024.10705707(1-6)Online publication date: 1-Sep-2024
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