ISSN 1000-1239 CN 11-1777/TP

• 系统结构 •

### DLPF：基于异构体系结构的并行深度学习编程框架

1. 1(国防科学技术大学并行与分布处理国防重点实验室 长沙 410073);2(国防科学技术大学计算机学院 长沙 410073) (yqwang2013@163.com)
• 出版日期: 2016-06-01
• 基金资助:
国家自然科学基金项目(61125201,U1435219)

### DLPF: A Parallel Deep Learning Programming Framework Based on Heterogeneous Architecture

Wang Yueqing1, Dou Yong1, Lü Qi1, Li Baofeng2, Li Teng1

1. 1(Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha 410073);2(College of Computer, National University of Defense Technology, Changsha 410073)
• Online: 2016-06-01

Abstract: Deep learning plays an important role in machine learning field, and it has been widely used in various applications. The prospect of research and applications of deep learning are huge. However, deep learning also faces several challenges. Firstly, there are many tools in deep learning field, but these tools are not convenient to use for non-expert users because the installation and usage of them are really complex. Secondly, the diversity of deep learning is limited because the flexibility of existing deep learning models is not enough. Furthermore, the training time of deep learning is so long that the optimal hyper-parameters combination cannot be found in a short time. To solve these problems, we design a deep learning programming framework based on heterogeneous architecture in this paper. The programming framework establishes a unified module library which can be used to build a deep model through the visual interface conveniently. Besides, the framework also accelerates the basic modules on heterogeneous platform, and makes the speed of searching optimal hyper-parameters combination be faster. Experimental results show that the programming framework can construct deep models flexibly, and more importantly, it can achieve comparative classification results and better timing performance for a variety of applications. In addition, the framework can search optimal hyper-parameters efficiently and make us infer the relationship of all hyper-parameters.