期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 16, 期 3, 页码 2022-2028出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2950326
关键词
Computational modeling; Convolution; Deep learning; Image edge detection; Edge computing; Task analysis; Vegetation; Deep neural network; edge computing; residual network; tree-based deep model
类别
资金
- Research and Development (R&D) Program (Research Pooling Initiative), Ministry of Education, Riyadh, Saudi Arabia (RPI-KSU)
This article proposes a tree-based deep model for effective load distribution to edge devices without much loss of accuracy. The input image is divided into groups of volumes, and each volume is passed through a tree structure. The tree structure has many branches and levels, each of which is represented by a convolutional layer. The layers are independent of each other. Therefore, various edge devices can update the parameters of the layers in parallel independently. Experiments are performed using a benchmark dataset and a publicly available date fruits database. Experimental results show that the proposed model has a high information density by reducing the number of parameters without much loss of accuracy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据