MAUnet: Multiscale attention U-Net for effective IR drop prediction

Wang, M. orcid.org/0009-0005-6222-1778, Cheng, Y. orcid.org/0000-0003-2477-314X, Lin, Y. orcid.org/0009-0005-2158-3027 et al. (4 more authors) (2024) MAUnet: Multiscale attention U-Net for effective IR drop prediction. In: DAC '24: Proceedings of the 61st ACM/IEEE Design Automation Conference. DAC '24: 61st ACM/IEEE Design Automation Conference, 23-27 Jun 2024, San Francisco CA, USA. ACM , pp. 1-6. ISBN 9798400706011

Abstract

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2024 Copyright held by the owner/author(s).| ACM 2024. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in DAC '24: Proceedings of the 61st ACM/IEEE Design Automation Conference, http://dx.doi.org/10.1145/3649329.3658465

Keywords: IR drop analysis; U-net; attention mechanism; machine learning
Dates:
  • Published: 7 November 2024
  • Published (online): 7 November 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematical and Physical Sciences
Depositing User: Symplectic Sheffield
Date Deposited: 19 Dec 2024 10:55
Last Modified: 19 Dec 2024 11:04
Status: Published
Publisher: ACM
Refereed: Yes
Identification Number: 10.1145/3649329.3658465
Open Archives Initiative ID (OAI ID):

Export

Statistics