Paper Info
Title
A survey on deploying mobile deep learning applications: A systemic and technical perspective
Abstract
With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network operators and service providers, and rapidly becomes a main research focus. In recent years, deep learning has achieved tremendous success in image processing, natural language processing, language analysis and other research fields. Despite the task performance has been greatly improved, the resources required to run these models have increased significantly. This poses a major challenge for deploying such applications on resource-restricted mobile devices. Mobile intelligence needs faster mobile processors, more storage space, smaller but more accurate models, and even the assistance of other network nodes. To help the readers establish a global concept of the entire research direction concisely, we classify the latest works in this field into two categories, which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks. We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications. Finally, we conjecture what the future may hold for deploying deep learning applications on mobile devices research, which may help to stimulate new ideas.
Year
DOI
Venue
2022
10.1016/j.dcan.2021.06.001
DIGITAL COMMUNICATIONS AND NETWORKS
Keywords
DocType
Volume
Deep learning, Mobile computing, Distributed offloading, Distributed caching
Journal
8
Issue
ISSN
Citations 
1
2468-5925
1
PageRank 
References 
Authors
0.38
0
7
Name
Order
Citations
PageRank
Yingchun Wang110.38
Jingyi Wang211.39
Weizhan Zhang310118.64
Yufeng Zhan412711.00
Song Guo53431278.71
Qinghua Zheng61261160.88
Xuanyu Wang710.38