Faculty
Email：ljiang_cs@sjtu.edu.cn
Institute：Institute of Scalable Computing
Brief Introduction
Li Jiang is a Research Professor at Shanghai Jiao Tong University. He is also Director of the Data Communication Processor Laboratory at Huawei Technologies Co., Ltd. and a senior expert/chief scientist in key technologies for communication processors. He was selected for the National Young Talent Program.
His research focuses on computer architecture and electronic design automation, with particular emphasis on AI infrastructure, such as, AI processor and accelerator design, inference frameworks and compilers, heterogeneous accelerator design, processing-in-memory/computing-in-memory architectures, and hardware-software co-design. He has published more than 140 papers in leading journals and conferences, including IEEE/ACM Transactions, ISCA, MICRO, HPCA, ASPLOS, DAC, AAAI, and ICCV. His work has received two DATE Best Paper Awards, ACM MM outstanding paper, one Best Ph.D. Dissertation Award, and multiple best paper nominations in ISCA, ICCAD and DATE.
He has led or participated in more than ten national, provincial, and industry-sponsored projects, including the National Key R&D Program, National Natural Science Foundation of China projects, Shanghai AI Laboratory/Shanghai Qi Zhi Institute projects, and Shanghai Natural Science Foundation projects. He has also undertaken more than twenty industry projects with companies including Huawei, Alibaba Cloud, Ant Group, and ZTE. Several of his techniques have been deployed or evaluated in industrial product lines.
He has served as a TPC member or co-chair for conferences including MICRO, DATE, ASP-DAC, ITC-Asia, ATS, CFTC, and CTC. He is a member of the MindSpore Technical Committee and serves on the editorial boards of IET Computers & Digital Techniques and Integration, the VLSI Journal. He is also a member of the editorial committee for the Artificial Intelligence Practice course and textbook series published by Higher Education Press.
Education
- 2008-2013, The Chinese University of Hong Kong, Ph.D. in Computer Science and Engineering
- 2003-2007, Shanghai Jiao Tong University, B.S. in Computer Science and Technology
Professional Experience
- 2024-present, Shanghai Jiao Tong University, Research Professor, School of Computer Science and Engineering
- 2017-2024, Shanghai Jiao Tong University, Associate Professor, Department of Computer Science and Engineering
- 2014-2017, Shanghai Jiao Tong University, Lecturer, Department of Computer Science and Engineering
- 2012-2013, Duke University, Visiting Scholar
Teaching Assignment
- 2024-2025 | 2, GPU computing and deep learning
- 2024-2025 | 1, Algorithm design and analysis
- 2024-2025 | 1, AI system optimization
Publications
## Full Publication List
https://dblp.uni-trier.de/pid/45/4954-2.html
## Selected Recent Publications
### 2026
- Zhixiong Zhao, Fangxin Liu, Junjie Wang, Chenyang Guan, Zongwu Wang, Li Jiang, and Haibing Guan. "SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization." AAAI, 2026.
- Zongwu Wang, Zhongyi Tang, Fangxin Liu, Chenyang Guan, Li Jiang, and Haibing Guan. "TFLOP: Towards Energy-Efficient LLM Inference An FPGA-Affinity Accelerator with Unified LUT-based OPtimization." ASP-DAC, 2026.
- Yilong Zhao, Fangxin Liu, Zongwu Wang, Mingjian Li, Mingxing Zhang, Chixiao Chen, and Li Jiang. "BLADE: Boosting LLM Decoding's Communication Efficiency in DRAM-based PIM." ASP-DAC, 2026.
- Junjie Wang, Fangxin Liu, Jinqi Zhu, Chenyang Guan, Tao Yang, Li Jiang, and Haibing Guan. "When Low-Rank Meets Mixed-Precision: Training-Free Joint Compression for Efficient LLM Inference." ASP-DAC, 2026.
- Houshu He, Naifeng Jing, Li Jiang, Xiaoyao Liang, and Zhuoran Song. "AGS: Accelerating 3D Gaussian Splatting SLAM via CODEC-Assisted Frame Covisibility Detection." ASPLOS, 2026.
- Fangxin Liu, Ning Yang, Jingkui Yang, Zongwu Wang, Chenyang Guan, Yu Feng, Li Jiang, and Haibing Guan. "EARTH: An Efficient MoE Accelerator with Entropy-Aware Speculative Prefetch and Result Reuse." ASPLOS, 2026.
- Haomin Li, Yun Liang, Fangxin Liu, Bowen Zhu, Zongwu Wang, Yu Feng, Liqiang Lu, Li Jiang, and Haibing Guan. "ORANGE: Exploring Ockham's Razor for Neural Rendering by Accelerating 3DGS on NPUs with GEMM-Friendly Blending and Balanced Workloads." HPCA, 2026.
- Hanjing Shen, Fangxin Liu, Jian Liu, Li Jiang, and Haibing Guan. "BEEMS: Boosting Machine Vision Efficiency via Computation Graph-Based Memory Smoothing." PPoPP, 2026.
### 2025
- Haomin Li, Fangxin Liu, Yichi Chen, Zongwu Wang, Shiyuan Huang, Ning Yang, Dongxu Lyu, and Li Jiang. "FATE: Boosting the Performance of Hyper-Dimensional Computing Intelligence with Flexible Numerical DAta TypE." ISCA, 2025.
- Houshu He, Gang Li, Fangxin Liu, Li Jiang, Xiaoyao Liang, and Zhuoran Song. "GSArch: Breaking Memory Barriers in 3D Gaussian Splatting Training via Architectural Support." HPCA, 2025.
- Fangxin Liu, Shiyuan Huang, Ning Yang, Zongwu Wang, Haomin Li, and Li Jiang. "CROSS: Compiler-Driven Optimization of Sparse DNNs Using Sparse/Dense Computation Kernels." HPCA, 2025.
- Fangxin Liu, Haomin Li, Bowen Zhu, Zongwu Wang, Zhuoran Song, Haibing Guan, and Li Jiang. "ASDR: Exploiting Adaptive Sampling and Data Reuse for CIM-based Instant Neural Rendering." ASPLOS, 2025.
- Fangxin Liu, Haomin Li, Zongwu Wang, Bo Zhang, Mingzhe Zhang, Shoumeng Yan, Li Jiang, and Haibing Guan. "ALLMod: Exploring Area-Efficiency of LUT-based Large Number Modular Reduction via Hybrid Workloads." DAC, 2025.
- Zongwu Wang, Peng Xu, Fangxin Liu, Yiwei Hu, Qingxiao Sun, Gezi Li, Cheng Li, Xuan Wang, Li Jiang, and Haibing Guan. "MILLION: MasterIng Long-Context LLM Inference Via Outlier-Immunized KV Product QuaNtization." DAC, 2025.
- Fangxin Liu, Ning Yang, Zongwu Wang, Xuanpeng Zhu, Haidong Yao, Xiankui Xiong, Li Jiang, and Haibing Guan. "BLOOM: Bit-Slice Framework for DNN Acceleration with Mixed-Precision." DAC, 2025.
- Haomin Li, Fangxin Liu, Zongwu Wang, Ning Yang, Shiyuan Huang, Xiaoyao Liang, Haibing Guan, and Li Jiang. "Attack and Defense: Enhancing Robustness of Binary Hyper-Dimensional Computing." ACM Transactions on Architecture and Code Optimization, 2025.
- Jiahao Sun, Yijian Zhang, Yuzhuo Liu, Fangxin Liu, Li Jiang, and Rui Yang. "A Sub-10 μs In-Memory-Search Collision Detection Accelerator Based on RRAM-TCAMs." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2025.
- Shiyuan Huang, Fangxin Liu, Tian Li, Zongwu Wang, Ning Yang, Haomin Li, and Li Jiang. "STCO: Enhancing Training Efficiency via Structured Sparse Tensor Compilation Optimization." ACM Transactions on Design Automation of Electronic Systems, 2025.
Awards
- 2024, Second Prize, Fourth National College Teacher Teaching Innovation Competition, Emerging Engineering, Associate Senior Faculty Group, team award, ranked 4th
- 2024, Shanghai Science and Technology Progress Award, "Key Technologies and Applications of Intelligent Computing for Industrial Internet Data"
- 2024, Special Prize, Fourth Shanghai College Teacher Teaching Innovation Competition, Emerging Engineering Group, team award, ranked 3rd
- 2023, National First-Class Undergraduate Online Course, teaching team award, ranked 4th
- 2023, National Young Talent Program
- 2023, DATE Best Paper Award
- 2022, Huawei Spark Award
- 2022, DATE Best Paper Award
- 2021, Outstanding Contribution Award, Ministry of Education-Huawei Intelligent Base Program
- 2021, Second Prize, Wu Wenjun AI Science and Technology Award, AI Chip category
- 2020, Shanghai Jiao Tong University Undergraduate Teaching Achievement Award, team award
- 2019, CCF Integrated Circuit Early Career Award
- 2019, ACM Shanghai Rising Star Award
- 2015, IEEE TTTC E. J. McCluskey Doctoral Thesis Award, Asia final first place and global finalist
- 2014, ATS Best Ph.D. Dissertation Award
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