Edge Intelligence based Co-training of CNN

Feiyi Xie, Aidong Xu, Yixin Jiang, Songlin Chen, Runfa Liao, Hong Wen
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引用次数: 8

Abstract

Improving training efficiency is a long-term major topic of neural network. With the popularity of intelligent terminal devices, such as smart phones and smart household electrical appliances, edge computing is gradually developing to edge intelligence (EI), which provides support to multiple terminals and play a role of a mini cloud. By making full use of the computing power of intelligent terminals in EI, while cooperating with edge servers, the training efficiency of convolution neural network (CNN) can effectively be improved. In this paper, an EI based co-training model between the edge server and intelligent terminals is proposed. The edge server and terminals propagate part of the CNN separately to accelerate the training of the CNN. Running time is greatly cut down by the division of CNN and simultaneous propagation of multiple terminals.
基于边缘智能的CNN协同训练
提高训练效率是神经网络长期研究的重要课题。随着智能手机、智能家电等智能终端设备的普及,边缘计算逐渐向边缘智能(EI)发展,为多个终端提供支持,起到迷你云的作用。通过充分利用EI中智能终端的计算能力,同时配合边缘服务器,可以有效提高卷积神经网络(CNN)的训练效率。提出了一种基于EI的边缘服务器与智能终端的协同训练模型。边缘服务器和终端分别传播部分CNN,加速CNN的训练。通过对CNN的分割和多终端同时传播,大大缩短了运行时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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