Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
