Abstract
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.
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