People

Mingjie Liu

Mingjie Liu is currently a Research Scientist at NVIDIA, where he actively conduct research on Electronic Design Automation. He received his PhD degree in electrical and computer engineering from the The University of Texas at Austin in 2022. His research interest include applied machine learning for design automation and design automation for analog and mixed-signal integrated circuits.

Research Area(s)
Artificial Intelligence and Machine Learning
Circuits and VLSI Design
Main Field of Interest
Circuits and VLSI Design
Google Scholar
https://scholar.google.com/citations?user=-v5DbrMAAAAJ&hl=en&oi=ao

Publications

2023

ChipNeMo: Domain-Adapted LLMs for Chip Design
Mingjie Liu, Teo Ene, Robert Kirby, Chris Cheng, Nathaniel Pinckney, Rongjian Liang, Jonah Alben, Himyanshu Anand, Sanmitra Banerjee, Ismet Bayraktaroglu, Bonita Bhaskaran, Bryan Catanzaro, Arjun Chaudhuri, Sharon Clay, Bill Dally, Laura Dang, Parikshit Deshpande, Siddhanth Dhodhi, Sameer Halepete, Eric Hill, Jiashang Hu, Sumit Jain, Brucek Khailany, George Kokai, Kishor Kunal, Xiaowei Li, Charley Lind, Hao Liu, Stuart Oberman, Sujeet Omar, Sreedhar Pratty, Jonathan Raman, Ambar Sarkar, Zhengjiang Shao, Hanfei Sun, Pratik P Suthar, Varun Tej, Walker Turner, Kaizhe Xu, Haoxing (Mark) Ren
VerilogEval: Evaluating Large Language Models for Verilog Code Generation
Mingjie Liu, Nathaniel Pinckney, Brucek Khailany, Haoxing (Mark) Ren
2023 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)
A 9.7fJ/Conv.-Step Capacitive Sensor Readout Circuit with Incremental Zoomed Time Domain Quantization
Zilong Shen, Xiyuan Tang, Zhongyi Wu, Haoyang Luo, Zongnan Wang, Mingjie Liu, Xing Zhang, Yuan Wang
2023 IEEE Custom Integrated Circuits Conference (CICC)

2022

An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design
Mingjie Liu, Haoyu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Selim Dogru, Anima Anandkumar, David Z. Pan, Brucek Khailany, Haoxing (Mark) Ren
Workshop on ML for Systems at NeurIPS
Why are Graph Neural Networks Effective for EDA Problems?
Haoxing (Mark) Ren, Siddhartha Nath, Yanqing Zhang, Hao Chen, Mingjie Liu
2022 International Conference on Computer-Aided Design