Research and Application of Edge Computing Based on Deep Learning

With the exponential growth of IoT terminals, smartphones, and wearable devices, traditional centralized cloud computing models have been unable to efficiently process data generated by edge devices. In order to meet the challenges, edge computing has been proposed. The location close to the termina...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2020-09, Vol.1646 (1), p.12016
Main Author: Cui, Wei
Format: Article
Language:English
Subjects:
Cloud computing
Deep learning
Edge computing
Electronic devices
Network latency
Physics
Smartphones
Wearable technology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the exponential growth of IoT terminals, smartphones, and wearable devices, traditional centralized cloud computing models have been unable to efficiently process data generated by edge devices. In order to meet the challenges, edge computing has been proposed. The location close to the terminal device meets the high computing volume, low latency, privacy requirements of deep learning on edge devices, bandwidth advantages, efficiency and scalability. We first introduced the background and motivation for running AI at the edge of the network, reviewed the basic concepts of deep learning, and then we provided the overall architecture of edge computing based on deep learning. We discussed three computing and inference models on terminal devices, edge servers and cross-edge devices, and describes the method to improve and optimize the edge deep learning model. Finally, we discuss the application scenarios and future opportunities of edge deep learning.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1646/1/012016