The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Machine Learning for Electronic Design Automation: A Survey
Open accessAbstract
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated is increasing. Although the application of machine learning (ML) techniques in electronic design automation (EDA) can trace its history back to the 1990s, the recent breakthrough of ML and the increasing complexity of EDA tasks have aroused more interest in incorporating ML to solve EDA tasks. In this article, we present a comprehensive review of existing ML for EDA studies, organized following the EDA hierarchy.
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Cites
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Mastering the game of Go without human knowledge
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Cited by
Perez-Cerrolaza, J., Abella, J., Borg, M., Donzella, C., Cerquides, J., Cazorla, F. J., Englund, C., Tauber, M., Nikolakopoulos, G., & Flores, J. L. (2024) · ACM Computing Surveys · doi:10.1145/3626314 · Cited by 185
Metadata
- Authors
- Huang, G., Hu, J., He, Y., Liu, J., Ma, M., Shen, Z., Wu, J., Xu, Y., Zhang, H., Zhong, K., Ning, X., Ma, Y., Yang, H., Yu, B., Yang, H., & Wang, Y.
- Venue
- ACM Transactions on Design Automation of Electronic Systems
- Published
- 2021-06-05
- Volume
- 26
- Issue
- 5
- Pages
- 1-46
- ISSN
- 1084-4309 (ISSN-L)
- 1557-7309
- OpenAlex
- W3169517138
- Cited by
- 341
- References
- 70
- FWCI
- 13.61
- Type
- journal article
- Language
- en
- Open access
- green
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- Huang, G., Hu, J., He, Y., Liu, J., Ma, M., Shen, Z., Wu, J., Xu, Y., Zhang, H., Zhong, K., Ning, X., Ma, Y., Yang, H., Yu, B., Yang, H., & Wang, Y. (2021). Machine Learning for Electronic Design Automation: A Survey. ACM Transactions on Design Automation of Electronic Systems. https://doi.org/10.1145/3451179
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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