Capuchin
Xuan Peng, Xuanhua Shi, Hulin Dai, Hai Jin, Weiliang Ma, Qian Xiong, Fan Yang, Xuehai Qian · 2020
In recent years, deep learning has gained unprecedented success in various domains, the key of the success is the larger and deeper deep neural networks (DNNs) that achieved very high accuracy. On the other side, since GPU global memory is a scarce resource, large models also pose a significant challenge due to memory requirement in the training process. This restriction limits the DNN architecture exploration flexibility.