Introduction :

Li Gang is an associate professor at the Institute of Automation, Chinese Academy of Sciences. He received his Bachelor's degree from the School of Automation, Northwestern Polytechnical University in 2013. After that, he got the Master's and Ph.D degrees from the Institute of Automation, Chinese Academy of Sciences in 2018 and 2021, respectively. From September 2021 to July 2024, he worked as a postdoctoral researcher at the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University.

His primary research interests are in computer architecture and deep learning. Li Gang has published nearly 30 papers in top international conferences and journals such as MICRO, HPCA, ASPLOS, TCAD, DAC, DATE, TCAS-II, TNNLS, ICCV, ECCV, ACL, AAAI, and ICLR. His work was awarded Best Paper at DATE'23 and DATE'24, and received a Best Paper Nomination at DATE'21. During his doctoral studies, Li Gang concurrently served as the Chief Architect for the AiRiA in Nanjing, leading a team that successfully developed the Quantized Neural Processing Unit (QNPU) chip.

Papers :

• Zeyu Zhu, Fanrong Li, Gang Li, Zejian Liu, Zitao Mo, Qinghao Hu, Xiaoyao Liang, Jian Cheng. MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantization. HPCA 2024 (CCF-A，Corresponding Author)
• Zeyu Zhu, Peisong Wang, Qinghao Hu, Gang Li, Xiaoyao Liang, Jian Cheng. FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale. ASPLOS 2024 (CCF-A)
• Xueyuan Liu, Zhuoran Song, Guohao Dai, Gang Li, Can Xiao, Yan Xiang, Dehui Kong, Ke Xu, and Xiaoyao Liang. FusionArch: A Fusion-Based Accelerator for Point-Based Point Cloud Neural Networks. DATE 2024 (CCF-B，Best Paper)
• Xiaolong Lin, Gang Li, Zizhao Liu, Yadong Liu, Fan Zhang, Zhuoran Song, Naifeng Jing, Xiaoyao Liang. AdaS: A Fast and Energy-Efficient CNN Accelerator Exploiting Bit-Sparsity. DAC 2023 (CCF-A，Corresponding Author)
• Zhuoran Song, Heng Lu, Gang Li, Li Jiang, Naifeng Jing, Xiaoyao Liang. PRADA: Point Cloud Recognition Acceleration via Dynamic Approximation. DATE 2023 (CCF-B，Best Paper)
• Qinghao Hu, Gang Li, Qiman Wu, Jian Cheng. PalQuant: Accelerating High-Precision Networks on Low-Precision Accelerators. ECCV 2022 (CCF-B，Co-first Author)
• Gang Li, Weixiang Xu, Zhuoran Song, Naifeng Jing, Jian Cheng, Xiaoyao Liang. Ristretto: An Atomized Processing Architecture for Sparsity-Condensed Stream Flow in CNN. MICRO 2022 (CCF-A)
• Gang Li, Zejian Liu, Fanrong Li, Jian Cheng. Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA. TCAD 2021 (CCF-A)
• Zejian Liu, Gang Li, Jian Cheng. Hardware Acceleration of Fully Quantized BERT for Efficient Natural Language Processing. DATE 2021 (CCF-B，Best Paper Candidate)
• Gang Li, Peisong Wang, Zejian Liu, Cong Leng, Jian Cheng. Hardware Acceleration of CNN with One-Hot Quantization of Weights and Activations. DATE 2020 (CCF-B)
• Gang Li, Fanrong Li, Tianli Zhao, Jian Cheng. Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA. DATE 2018 (CCF-B)
• Jian Cheng, Peisong Wang, Gang Li, Qinghao Hu, Hanqing Lu. Recent Advances in Efficient Computation of Deep Convolutional Neural Networks. FITEE 2018 (CCF-C)

Awards :

• Best Paper at DATE’24
• Best Paper at DATE’23
• Best Paper Candidate at DATE’21