Xingquan Li (李兴权)
Associate Professor, PhD
Pengcheng Laboratory (PCL) | Chinese University of Hong Kong (CUHK)
Research interests: Electronic Design Automation (EDA), AI for EDA, AI for Algorithm Design
Associate Professor, PhD
Pengcheng Laboratory (PCL) | Chinese University of Hong Kong (CUHK)
Research interests: Electronic Design Automation (EDA), AI for EDA, AI for Algorithm Design
Dr. Xingquan Li is an associate professor at Pengcheng Laboratory (PCL). He received the Ph.D. degree from Fuzhou University in 2018. His research interests include Electronic Design Automation (EDA) and AI for EDA.
His team has developed an open-source infrastructure of EDA (iEDA) and an AI-aided design library for EDA (AiEDA). He has published over 70 papers in journals and conferences such as TCAD, TC, TVLSI, TODAES, SCIENCE CHINA, DAC, ICCAD, DATE, ICCD, ASP-DAC, ISPD, ISCAS, ISEDA, and NeurIPS, and has filed 21 invention patents.
He has achieved first-place awards from ICCAD@CAD Contest three times in 2017, 2018, and 2022. In 2020, he was honored with the Operational Research Application Award from the Chinese Operations Research Society. In 2023, he received the Best Paper Award from ISEDA.
Leading the development of iEDA (open-source EDA infrastructure), contributing to the open-source EDA community. Research on advanced EDA algorithms and methodologies
Development of AI-aided design library (AiEDA) and vector dataset (iDATA), including machine learning models for design optimization, prediction, and automated design space exploration.
Research on AI-aided algorithm design, including machine learning models for algorithm optimization, prediction, and automated algorithm design space exploration.
An Open-Source Intelligent Physical Implementation Toolchain
iEDA is an open-source infrastructure for EDA. It provides a comprehensive toolkit for physical implementation of integrated circuits, including placement, routing, timing optimization, and DRC.
An Open-Source AI-Aided Design Library for Design-to-Vector
AiEDA is an AI-aided design library that enables design-to-vector conversion for EDA. It leverages machine learning and deep learning techniques to optimize chip design processes.
Large Layout Model for Chip Design
Pre-training for chip layout design, building a large foundation model specialized for chip layout design, achieving chip layout generation without any manual intervention and EDA tools.
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ACM Transactions on Design Automation of Electronic Systems (TODAES), 2025 (CCF-B)
Proceedings of the 29th Asia and South Pacific Design Automation Conference (ASP-DAC), 2026 (CCF-C)
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2025 (CCF-A)
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2025 (CCF-A)
ACM Transactions on Design Automation of Electronic Systems (TODAES), 2025 (CCF-B)
ACM Transactions on Design Automation of Electronic Systems (TODAES), 2025 (CCF-B)
ACM Transactions on Design Automation of Electronic Systems (TODAES), 2025 (CCF-B)
ACM Transactions on Design Automation of Electronic Systems (TODAES), 2025 (CCF-B)
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2025 (CCF-A)
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2025 (CCF-A)
Proceedings of Design Automation Conference (DAC), 2025 (CCF-A)
Proceedings of Design Automation Conference (DAC), 2025 (CCF-A)
IEEE/ACM Design, Automation and Test in Europe Conference (DATE), 2025 (CCF-B)
Proceedings of the 28th Asia and South Pacific Design Automation Conference (ASP-DAC), 2025 (CCF-C)
Chinese University of Hong Kong (CUHK)
Research focus: AI for EDA
Fuzhou University
Research area: Electronic Design Automation
Fuzhou University
Major: Allpied Mathmatics
Pengcheng Laboratory (PCL), Shenzhen, China
The Chinese University of Hong Kong (CUHK), Hong Kong, China
Associate Professor, Pengcheng Laboratory (PCL)
The Chinese University of Hong Kong (CUHK)