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Award ID contains: 1718570

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  1. ; ; ; ; ; ; ; ; (, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)
    Full Text Available 
  2. ; ; ; ; ; ; ; ; (, 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE))
    Full Text Available 
  3. ; ; ; ; ; ; (, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)
  4. (, MLCAD '22: Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD)
    Long-tailed distribution is a common and critical issue in the field of machine learning. While prior work addressed data imbalance in several tasks in electronic design automation (EDA), insufficient attention has been paid to the long-tailed distribution in real-world EDA problems. In this paper, we argue that conventional performance metrics can be misleading, especially in EDA contexts. Through two public EDA problems using convolutional neural networks and graph neural networks, we demonstrate that simple yet effective model-agnostic methods can alleviate the issue induced by long-tailed distribution when applying machine learning algorithms in EDA. 
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  5. ; ; ; ; ; ; (, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)
    Full Text Available 
  6. ; ; ; ; ; (, IEEE/ACM Asian and South Pacific Design Automation Conference (ASP-DAC))
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  7. ; ; ; ; ; ; ; (, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)
    null (Ed.)
    Full Text Available 
  8. (, IEEE/ACM International Conference on Computer-Aided Design (ICCAD))
    null (Ed.)
    Accepted Manuscript Full Text Available
  9. (, Design Automation Conference)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; (, ISPD '20: Proceedings of the 2020 International Symposium on Physical Design)
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