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Creators/Authors contains: "Li, Yingjie"

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  1. ; ; ; ; (, International Conference on Machine Learning (ICML))
    Free, publicly-accessible full text available July 21, 2025
  2. ; ; ; ; (, Proceedings of the 41st International Conference on Machine Learning (ICML))
    Free, publicly-accessible full text available July 13, 2025
  3. ; ; ; ; ; ; ; (, ACM Transactions on Design Automation of Electronic Systems)
    With the ever-increasing hardware design complexity comes the realization that efforts required for hardware verification increase at an even faster rate. Driven by the push from the desired verification productivity boost and the pull from leap-ahead capabilities of machine learning (ML), recent years have witnessed the emergence of exploiting ML-based techniques to improve the efficiency of hardware verification. In this article, we present a panoramic view of how ML-based techniques are embraced in hardware design verification, from formal verification to simulation-based verification, from academia to industry, and from current progress to future prospects. We envision that the adoption of ML-based techniques will pave the road for more scalable, more intelligent, and more productive hardware verification. 
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    Free, publicly-accessible full text available July 31, 2025
  4. ; ; ; (, 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD))
    Full Text Available 
  5. ; ; ; (, IEEE)
    Full Text Available 
  6. ; ; ; ; ; (, IEEE)
    Full Text Available 
  7. ; ; (, iScience)
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  8. ; ; ; (, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)
    Full Text Available 
  9. ; ; (, Geography and Sustainability)
    Full Text Available 
  10. ; ; ; ; ; (, Journal of Open Source Software)
    Full Text Available