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
Abstract
Automatic transistor sizing in circuit design continues to be a formidable challenge. Despite that Bayesian optimization (BO) has achieved significant success, it is circuit-specific, limiting the accumulation and transfer of design knowledge for broader applications. This paper proposes (1) efficient automatic kernel construction, (2) the first transfer learning across different circuits and technology nodes for BO, and (3) a selective transfer learning scheme to ensure only useful knowledge is utilized. These three novel components are integrated into BO with Multi-objective Acquisition Ensemble (MACE) to form Knowledge Alignment and Transfer Optimization (KATO) to deliver state-of-the-art performance: up to 2x simulation reduction and 1.2x design improvement over the baselines.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: | |
Copyright, Publisher and Additional Information: | © 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: |
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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): | oai:eprints.whiterose.ac.uk:221015 |