KATO: Knowledge alignment and transfer for transistor sizing of different design and technology

Xing, W.W. orcid.org/0000-0002-3177-8478, Fan, W. orcid.org/0009-0006-4522-2194, Liu, Z. orcid.org/0009-0001-2415-793X et al. (2 more authors) (2024) KATO: Knowledge alignment and transfer for transistor sizing of different design and technology. 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

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Item Type: Proceedings Paper
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© 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/10.1145/3649329.3657380.

Keywords: transistor sizing; transfer learning; bayesian optimization
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 11:04
Last Modified: 19 Dec 2024 11:04
Status: Published
Publisher: ACM
Refereed: Yes
Identification Number: 10.1145/3649329.3657380
Open Archives Initiative ID (OAI ID):

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