An empirical study towards characterizing deep learning development and deployment across different frameworks and platforms

• Main Authors: GUO, Qianyu, CHEN, Sen, XIE, Xiaofei, MA, Lei, HU, Qiang, LIU, Hongtao, LIU, Yang, ZHAO, Jianjun, LI, Xiaohong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Deep learning frameworks; Deep learning platforms; Deep learning deployment; Empirical study; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7069; https://ink.library.smu.edu.sg/context/sis_research/article/8072/viewcontent/ASE.2019.00080.pdf
• Institution: Singapore Management University

Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks and platforms play a key role to catalyze such progress. However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL software development...