Dr. Nan Wu

Assistant Professor

Contact:

Email: Dr. Nan Wu

Office Phone: 202-994-0126

SEH 6590 | Office hours: Wednesday 4:00-5:00 PM or by appointment

Dr. Nan Wu’s research interests lie in the joint area among computer architecture, electronic design automation (EDA), and machine learning (ML). She focuses on hardware agile development empowered by ML and studies how to infuse intelligence, improve agility, and eventually enable no-human-in-the-loop automation for scalable and efficacious hardware development flow by synergistic investigation across algorithm, architecture, and EDA.

Education

Ph.D., Electrical and Computer Engineering, University of California, Santa Barbara, 2023
M.S., Electrical and Computer Engineering, University of California, Santa Barbara, 2018
B.S., Electronic Engineering, Tsinghua University, China, 2012
B.S., Economics, Tsinghua University, China, 2012

Areas of Expertise

Machine Learning for Computer Systems
Electronic Design Automation
Computer Architecture
Machine Learning
Hardware-Software Co-design

Publications

Selected Conference Papers:

Nan Wu, Yingjie Li, Cong Hao, Steve Dai, Cunxi Yu, Yuan Xie, “Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks”, the 60th ACM/IEEE Design Automation Conference (DAC), 2023.
Lakshmi Sathidevi, Abhinav Sharma, Nan Wu, Xun Jiao, Cong Hao, “PreAxC: Error Distribution Prediction for Approximate Computing Quality Control using Graph Neural Networks”, the International Symposium on Quality Electronic Design (ISQED), 2023.
Nan Wu, Yuan Xie, Cong Hao, “AI-assisted Synthesis in Next Generation EDA: Promises, Challenges, and Prospects”, the 40th IEEE International Conference on Computer Design (ICCD), 2022.
Haoyu Wang, Nan Wu, Hang Yang, Cong Hao, Pan Li, “Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation”, the 36th Annual Conference on Neural Information Processing Systems (NeurIPS), 2022.
Nan Wu, Hang Yang, Yuan Xie, Pan Li, Cong Hao, “High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing”, the 59th ACM/IEEE Design Automation Conference (DAC), 2022.
Nan Wu, Jiwon Lee, Yuan Xie, Cong Hao, “LOSTIN: Logic Optimization via Spatio-Temporal Information with Hybrid Graph Models”, the 33rd IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2022.
Nan Wu, Yuan Xie, Cong Hao, “IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning”, the Great Lakes Symposium on VLSI (GLSVLSI), 2021.
Nan Wu, Adrien Vincent, Dmitri Strukov, “Preliminary Results Towards Reinforcement Learning with Mixed-Signal Memristive Neuromorphic Circuits”, IEEE International Symposium on Circuits and Systems (ISCAS), 2019.

Selected Journal Papers:

Nan Wu, Yuan Xie, Cong Hao, “IronMan-Pro: Multi-objective Design Space Exploration in HLS via Reinforcement Learning and Graph Neural Network based Modeling”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2022.
Nan Wu, Yuan Xie, “A Survey of Machine Learning for Computer Architecture and Systems”, ACM Computing Surveys (CSUR), 2022.
Nan Wu, Lei Deng, Guoqi Li, Yuan Xie, “Core Placement Optimization for Multi-chip Many-core Neural Network Systems with Reinforcement Learning”, ACM Transactions on Design Automation of Electronic Systems (TODAES), 2020.

Awards

[2023] Best Paper Award, IEEE/ACM Design Automation Conference (DAC)
[2023] UCSB ECE Dissertation Fellowship
[2022] Best Paper Nomination, IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP)
[2021] Best Paper Award, ACM Great Lakes Symposium on VLSI (GLSVLSI)
[2016] Excellent Undergraduate Student, Tsinghua University