Sixu Li

I am Sixu Li ("Sixu" is pronounced as "si-hsu" or "sue-hsu"), currently a Ph.D. student at Georgia Institute of Technology, advised by Prof. Yingyan (Celine) Lin. I received the B.Eng. degree at the University of Electronic Science and Technology of China in 2021, advised by Prof. Jun Zhou.

I have published several papers in peer-reviewed journals & conferences, including TCAS-I/II, TBioCAS, ICCAD, ISCA, etc. I have led & participated in several tape-outs in 28nm, 40nm, 55nm, and 65nm.

My research interests are

Computer Architecture
Digital Circuits & System

Education

B.Eng. in Communication Engineering, University of Electronic Science and Technology of China, (电子科技大学), 2017 - 2021
Ph.D. Student in Electronic and Computer Engineering, Rice University, 2022 - 2023 (Transferred to Gatech)
Ph.D. Student in Computer Science, Georgia Institute of Technology, 2023 - Present

Internship Experience

Nvidia - Santa Clara
- Nvidia Research - ASIC & VLSI Research
- May. 2024 - : Research Intern
Sensetime - Beijing
- Research Center - Department of High-Performance Computing
- Jan. 2021 - Jun. 2021: Research Intern

Work Experience

Sensetime - Shanghai
- Research Center - Department of High-Performance Computing
- Jun. 2022 - Jul. 2022: System Researcher
- Jul. 2021 - May 2022: Assistant System Researcher

Service

Reviewer: IEEE Transactions on Biomedical Circuits and Systems (TBioCAS)
Reviewer: IEEE Transactions on Circuits and Systems II: Express Briefs (TCAS-II)

Selected Publications (Team Leader and/or Primary Contributor)

Instant-3D: Instant Neural Radiance Fields Training Towards Real-Time AR/VR 3D Reconstruction
- International Symposium on Computer Architecture (ISCA), 2023
- Sixu Li*, Chaojian Li*, Wenbo Zhu, Boyang (Tony) Yu, Yang (Katie) Zhao, Cheng Wan, Haoran You, Huihong Shi, Yingyan (Celine) Lin
- [arxiv] [ACM] [Slides]
RT-NeRF: Real-Time On-Device Neural Radiance Fields Towards Immersive AR/VR Rendering
- International Conference on Computer-Aided Design (ICCAD), 2022
- Chaojian Li*, Sixu Li*, Yang Zhao*, Wenbo Zhu, Yingyan Lin
- [arxiv] [ACM] [Slides]
An Ultra-Energy-Efficient and High Accuracy ECG Classification Processor with SNN Inference assisted by On-chip ANN Learning
- IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), 2022
- Ruixin Mao, Sixu Li, Zhaomin Zhang, Zihan Xia, Jianbiao Xiao, Zixuan Zhu, Jiahao Liu, Weiwei Shan, Liang Chang, Jun Zhou
- [IEEE]
A Fast and Energy-Efficient SNN Processor With Adaptive Clock/Event-Driven Computation Scheme and Online Learning
- IEEE Transactions on Circuits and Systems I: Regular Papers (TCAS-I), 2021
- Sixu Li, Zhaomin Zhang, Ruixin Mao, Jianbiao Xiao, Liang Chang, Jun Zhou
- [IEEE]