no code implementations • 11 Mar 2022 • Zhuoran Song, Yihong Xu, Han Li, Naifeng Jing, Xiaoyao Liang, Li Jiang
The training phases of Deep neural network~(DNN) consumes enormous processing time and energy.
1 code implementation • 9 Mar 2022 • Zhuoran Song, Yihong Xu, Zhezhi He, Li Jiang, Naifeng Jing, Xiaoyao Liang
We explore the sparsity in ViT and observe that informative patches and heads are sufficient for accurate image recognition.
1 code implementation • 19 Oct 2018 • Haiyue Song, Chengwen Xu, Qiang Xu, Zhuoran Song, Naifeng Jing, Xiaoyao Liang, Li Jiang
We thus propose a novel approximate computing architecture with a Multiclass-Classifier and Multiple Approximators (MCMA).
no code implementations • 23 May 2018 • Zhuoran Song, Ru Wang, Dongyu Ru, Hongru Huang, Zhenghao Peng, Jing Ke, Xiaoyao Liang, Li Jiang
In this paper, we propose the Approximate Random Dropout that replaces the conventional random dropout of neurons and synapses with a regular and predefined patterns to eliminate the unnecessary computation and data access.
