Machine Learning Empowered Agile Hardware Design and Design Automation

Aug 6, 2026

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Nan Wu’s dissertation investigates how machine learning can strengthen agile hardware design and design automation workflows. The work targets improvements across synthesis and verification quality while supporting scalable decision-making during hardware development. By integrating data-driven prediction with automated design steps, the research aims to enhance accuracy, efficiency, and robustness of engineering processes. The dissertation also evaluates enabling methods through published results on graph learning, approximate computing quality control, and AI-assisted synthesis in next-generation EDA.

LanguageEnglish

CategoryResearch & Report

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Pages193

Reading Time8 h 6 min

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Acknowledgements

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UC Santa Barbara UC Santa Barbara Electronic Theses and Dissertations Title Machine Learning Empowered Agile Hardware Design and Design Automation Permalink [https://escholarship.org/uc/item/0b22z4gt](https://escholarship.org/uc/item/0b22z4gt) Author Wu, Nan Publication Date 2023 Peer reviewed|Thesis/dissertation [eScholarship.org](eScholarship.org) Powered by the California Digital Library University of California University of California Santa Barbara Machine Learning Empowered Agile Hardware Design and Design Automation A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Electrical and Computer Engineering by Nan Wu Committee in charge: Professor Yuan Xie, Co-Chair Professor Peng Li, Co-Chair Professor Li-C Wang Professor Timothy Sherwood Professor Zheng Zhang Professor Yufei Ding Professor Cong Hao The Dissertation of Nan Wu is approved. | Professor Li-C Wang | | --- | | Professor Timothy Sherwood | | Professor Zheng Zhang | | Professor Yufei Ding | Professor Cong Hao Professor Peng Li, Committee Co-Chair Professor Yuan Xie, Committee Co-Chair May 2023 Machine Learning Empowered Agile Hardware Design and Design Automation Copyright © 2023 by Nan Wu Acknowledgements To all the amazing people sharing experiences and thoughts with me Thank you for shaping my philosophy on research and life Over the past few years, I am so privileged to have met many exceptional teachers and mentors, who guide me right paths to career and life, encourage me to pursue my goals, and vigorously support me all the way. I will not be where I am today without standing on their shoulders. I am incredibly grateful to my advisor, Prof. Yuan Xie, for many years of guidance and support throughout my academic journey, for always being available and present, for being so professional and caring at the same time, and for being a constant source of inspiration. I have learned a lot from your broad knowledge and big vision in research, as well as your wisdom and passion for life. You are certainly a role model for me in how to cultivate a successful career and how to become an exceptional person. You have taught me so much more than research skills and I aspire to become what you are to me to the next-generation blooming researchers. There is a Chinese proverb that states, \A teacher for a day is to be respected as a father for a lifetime". I am blessed with the best advisor from whom I can learn valuable lessons lifelong. I would like to convey my heartfelt gratitude to my thesis committee members, Prof. Peng Li, Prof. Li-C Wang, Prof. Timothy Sherwood, Prof. Zheng Zhang, Prof. Yufei Ding, and Prof. Cong Hao. Thank you, Prof. Peng Li, for being so patient, careful, and helpful throughout the entire journey. Thank you, Prof. Li-C Wang, for enlightening me with new research perspectives to examine the question that I have not thought about. Thank you, Prof. Timothy Sherwood, for your unwavering support and providing me with valuable insights for my research. Thank you, Prof. Zheng Zhang, for showing me the allure of conducting rigorous and meticulous research. Thank you, Prof. Yufei Ding, for your helpfulness and providing me with invaluable feedback on my research. Thankyou, Prof. Cong Hao, for being receptive to my random ideas, encouraging me to keep being proactive, and inspiring me to live bright and courageous womanhood. I am deeply indebted to all of my collaborators, Prof. Cong Hao, Prof. Cunxi Yu, Prof. Pan Li, Prof. Guoqi Li, and Prof. Lei Deng for their knowledgeable advice and insightful guidance. I sincerely thank the coauthors of my research papers, including but not limited to Prof. Dmitri Strukov, Dr. Steve Dai, Dr. Adrien Vincent, Yingjie Li, Haoyu Wang, Hang Yang, and Hanqiu Chen. I have been so fortunate to meet so many wonderful, intelligent, and diligent researchers at UCSB Seal-Lab, includ

FAQs

What is the dissertation’s main topic?

The dissertation focuses on using machine learning to empower agile hardware design and design automation.

What degree is the dissertation submitted for?

It is submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Electrical and Computer Engineering.

What kinds of research areas are reflected in the author’s publications?

The publications listed emphasize graph learning–based reasoning, error distribution prediction for approximate computing quality control using graph neural networks, and AI-assisted synthesis in next-generation EDA.

Aug 6, 2026

Upload by Patrick