| 2026 | CGO | Progressive Low-Precision Approximation of Tensor Operators on GPUs: Enabling Greater Trade-Offs between Performance and Accuracy. | Fan Luo, Guangli Li, Zhaoyang Hao, Xueying Wang, Xiaobing Feng, Huimin Cui, Jingling Xue |
| 2026 | CGO | PriTran: Privacy-Preserving Inference for Transformer-Based Language Models under Fully Homomorphic Encryption. | Yuechen Mu, Guangli Li, Shiping Chen, Jingling Xue |
| 2026 | CGO | DyPARS: Dynamic-Shape DNN Optimization via Pareto-Aware MCTS for Graph Variants. | Hao Qian, Guangli Li, Qiuchu Yu, Xueying Wang, Jingling Xue |
| 2026 | EuroPar | DACOS: Dependency-Aware Cross-Kernel Overlapping for Optimizing Short-Sequence Workloads in LLM Applications. | Zhaoyang Hao, Guangli Li, Fan Luo, Hao Qian, Xueying Wang, Jiacheng Zhao, Xiaobing Feng, Huimin Cui, Jingling Xue |
| 2026 | ICS | FHECrafter: A Multi-Agent Framework for Automated Fully Homomorphic Encrypted Tensor Program Generation. | Qiuchu Yu, Ruiyuan Xu, Guangli Li |
| 2025 | COLING | ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models. | Chenyang Song, Xu Han, Zhengyan Zhang, Shengding Hu, Xiyu Shi, Kuai Li, Chen Chen, Zhiyuan Liu, Guangli Li, Tao Yang, Maosong Sun |
| 2025 | EuroPar | TopServe: Task-Operator Co-scheduling for Efficient Multi-DNN Inference Serving on GPUs. | Ao Chen, Guangli Li, Feng Yu, Xueying Wang, Jiacheng Zhao, Huimin Cui, Xiaobing Feng, Jingling Xue |
| 2025 | NAACL | DDGIP: Radiology Report Generation Through Disease Description Graph and Informed Prompting. | Chentao Huang, Guangli Li, Xinjiong Zhou, Yafeng Ren, Hongbin Zhang |
| 2024 | ASPLOS | Optimizing Dynamic-Shape Neural Networks on Accelerators via On-the-Fly Micro-Kernel Polymerization. | Feng Yu, Guangli Li, Jiacheng Zhao, Huimin Cui, Xiaobing Feng, Jingling Xue |
| 2021 | CGO | Unleashing the Low-Precision Computation Potential of Tensor Cores on GPUs. | Guangli Li, Jingling Xue, Lei Liu, Xueying Wang, Xiu Ma, Xiao Dong, Jiansong Li, Xiaobing Feng |
| 2021 | ICPP | LoWino: Towards Efficient Low-Precision Winograd Convolutions on Modern CPUs. | Guangli Li, Zhen Jia, Xiaobing Feng, Yida Wang |
| 2021 | ISPA | Understanding the Runtime Overheads of Deep Learning Inference on Edge Devices. | Xiu Ma, Guangli Li, Lei Liu, Huaxiao Liu, Lei Liu, Xiaobing Feng |
| 2021 | ISPASS | Pinpointing the Memory Behaviors of DNN Training. | Jiansong Li, Xiao Dong, Guangli Li, Peng Zhao, Xueying Wang, Xiaobing Chen, Xianzhi Yu, Yongxin Yang, Zihan Jiang, Wei Cao, Lei Liu, Xiaobing Feng |
| 2021 | SC | G-SEPM: building an accurate and efficient soft error prediction model for GPGPUs. | Hengshan Yue, Xiaohui Wei, Guangli Li, Jianpeng Zhao, Nan Jiang, Jingweijia Tan |
| 2020 | EuroPar | Accelerating Deep Learning Inference with Cross-Layer Data Reuse on GPUs. | Xueying Wang, Guangli Li, Xiao Dong, Jiansong Li, Lei Liu, Xiaobing Feng |
| 2020 | ICASSP | Lance: efficient low-precision quantized winograd convolution for neural networks based on graphics processing units. | Guangli Li, Lei Liu, Xueying Wang, Xiu Ma, Xiaobing Feng |
| 2020 | ISPA | Characterizing the I/O Pipeline in the Deployment of CNNs on Commercial Accelerators. | Jiansong Li, Zihan Jiang, Fangxin Liu, Xiao Dong, Guangli Li, Xueying Wang, Wei Cao, Lei Liu, Yanzhi Wang, Tao Li, Xiaobing Feng |
| 2020 | NPC | Compiler-Assisted Operator Template Library for DNN Accelerators. | Jiansong Li, Wei Cao, Xiao Dong, Guangli Li, Xueying Wang, Lei Liu, Xiaobing Feng |
| 2019 | CGO | Accelerating GPU Computing at Runtime with Binary Optimization. | Guangli Li, Lei Liu, Xiaobing Feng |
| 2019 | HPCC | Swarm Intelligence Optimized Generative Model for Network Performance Prediction. | Chuanqi Jiang, Hua Wang, Jiaxin Yan, Xiaole Li, Guangli Li |
| 2019 | PPoPP | Exploiting the input sparsity to accelerate deep neural networks: poster. | Xiao Dong, Lei Liu, Guangli Li, Jiansong Li, Peng Zhao, Xueying Wang, Xiaobing Feng |
| 2018 | ICANN | Fast CNN Pruning via Redundancy-Aware Training. | Xiao Dong, Lei Liu, Guangli Li, Peng Zhao, Xiaobing Feng |
| 2018 | ICANN | Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge. | Guangli Li, Lei Liu, Xueying Wang, Xiao Dong, Peng Zhao, Xiaobing Feng |
| 2018 | IJCNN | Background Subtraction on Depth Videos with Convolutional Neural Networks. | Xueying Wang, Lei Liu, Guangli Li, Xiao Dong, Peng Zhao, Xiaobing Feng |