no code implementations • 3 Sep 2023 • Jiaxing Qi, Shaohan Huang, Zhongzhi Luan, Carol Fung, Hailong Yang, Depei Qian
In this work, we proposed LogGPT, a log-based anomaly detection framework based on ChatGPT.
no code implementations • 1 Jan 2022 • Shanjun Zhang, Mingzhen Li, Hailong Yang, Yi Liu, Zhongzhi Luan, Depei Qian
Currently, the DL compilers partition the input DL models into several subgraphs and leverage the auto-tuning to find the optimal tensor codes of these subgraphs.
1 code implementation • 6 Feb 2020 • Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Lin Gan, Guangwen Yang, Depei Qian
In this paper, we perform a comprehensive survey of existing DL compilers by dissecting the commonly adopted design in details, with emphasis on the DL oriented multi-level IRs, and frontend/backend optimizations.
no code implementations • 31 Dec 2019 • Ruiyuan Gao, Ming Dun, Hailong Yang, Zhongzhi Luan, Depei Qian
Existing research works rely on metrics that are either impractical or insufficient to measure the effectiveness of privacy protection methods in the above scenario, especially from the aspect of a single user.
1 code implementation • 28 May 2019 • Weicheng Li, Rui Wang, Zhongzhi Luan, Di Huang, Zidong Du, Yunji Chen, Depei Qian
Convolutional Neural Network (CNN) based Deep Learning (DL) has achieved great progress in many real-life applications.
no code implementations • 16 Apr 2019 • Mingzhen Li, Changxi Liu, Jianjin Liao, Xuegui Zheng, Hailong Yang, Rujun Sun, Jun Xu, Lin Gan, Guangwen Yang, Zhongzhi Luan, Depei Qian
The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability.