Towards Efficient Deep Neural Network Training by FPGA-Based Batch-Level Parallelism. - Cheng Luo; Man-Kit Sit; Hongxiang Fan; Shuanglong Liu; Wayne Luk; Ce Guo (FCCM, 2019)

Towards Improving the Performance of BFT Consensus For Future Permissioned Blockchains. - Manuel Bravo; Zsolt István; Man-Kit Sit (CoRR, 2020)

An experimental framework for improving the performance of BFT consensus for future permissioned blockchains. - Man-Kit Sit; Manuel Bravo; Zsolt István (DEBS, 2021)

Ekko: A Large-Scale Deep Learning Recommender System with Low-Latency Model Update. - Chijun Sima; Yao Fu; Man-Kit Sit; Liyi Guo; Xuri Gong; Feng Lin; Junyu Wu; Yongsheng Li; Haidong Rong; Pierre-Louis Aublin; Luo Mai (OSDI, 2022)

GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models. - Hanjing Wang; Man-Kit Sit; Congjie He; Ying Wen 001; Weinan Zhang 001; Jun Wang 012; Yaodong Yang 001; Luo Mai (ICML, 2023)

Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness. - Zeyuan Tan; Xiulong Yuan; Congjie He; Man-Kit Sit; Guo Li; Xiaoze Liu; Baole Ai; Kai Zeng 002; Peter R. Pietzuch; Luo Mai (CoRR, 2023)

MoE-CAP: Cost-Accuracy-Performance Benchmarking for Mixture-of-Experts Systems. - Yao Fu; Yinsicheng Jiang; Yeqi Huang; Ping Nie; Zhan Lu; Leyang Xue; Congjie He; Man-Kit Sit; Jilong Xue; Li Dong; Ziming Miao; Kai Zou; et al. (CoRR, 2024)

MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems. - Yinsicheng Jiang; Yao Fu; Yeqi Huang; Ping Nie; Zhan Lu; Leyang Xue; Congjie He; Man-Kit Sit; Jilong Xue; Li Dong; Ziming Miao; Dayou Du; et al. (CoRR, 2025)