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 • 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.
no code implementations • 15 Jan 2019 • Ming-Cheng Chen, Riling Li, Lin Gan, Xiaobo Zhu, Guangwen Yang, Chao-Yang Lu, Jian-Wei Pan
We show that low-depth random quantum circuits can be efficiently simulated by a quantum teleportation-inspired algorithm.
Quantum Physics
no code implementations • 9 May 2018 • Xi Zhang, Di Ma, Xu Ouyang, Shanshan Jiang, Lin Gan, Gady Agam
We show that by using masks the motion estimate results in a quadratic function of input features in the output layer.
1 code implementation • 21 Jul 2016 • Xi Zhang, Di Ma, Lin Gan, Shanshan Jiang, Gady Agam
In this paper we propose a novel extension to the SMOTE algorithm with a theoretical guarantee for improved classification performance.