Deep Learning on Computational-Resource-Limited Platforms: A Survey
Joint Authors
Yi, Yugen
Chen, Chunlei
Zhang, Huixiang
Zhang, Peng
Dai, Jiangyan
Zhang, Huihui
Zhang, Yonghui
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-01
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Telecommunications Engineering
Abstract EN
Nowadays, Internet of Things (IoT) gives rise to a huge amount of data.
IoT nodes equipped with smart sensors can immediately extract meaningful knowledge from the data through machine learning technologies.
Deep learning (DL) is constantly contributing significant progress in smart sensing due to its dramatic superiorities over traditional machine learning.
The promising prospect of wide-range applications puts forwards demands on the ubiquitous deployment of DL under various contexts.
As a result, performing DL on mobile or embedded platforms is becoming a common requirement.
Nevertheless, a typical DL application can easily exhaust an embedded or mobile device owing to a large amount of multiply and accumulate (MAC) operations and memory access operations.
Consequently, it is a challenging task to bridge the gap between deep learning and resource-limited platforms.
We summarize typical applications of resource-limited deep learning and point out that deep learning is an indispensable impetus of pervasive computing.
Subsequently, we explore the underlying reasons for the high computational overhead of DL through reviewing the fundamental concepts including capacity, generalization, and backpropagation of a neural network.
Guided by these concepts, we investigate on principles of representative research works, as well as three types of solutions: algorithmic design, computational optimization, and hardware revolution.
In pursuant to these solutions, we identify challenges to be addressed.
American Psychological Association (APA)
Chen, Chunlei& Zhang, Peng& Zhang, Huixiang& Dai, Jiangyan& Yi, Yugen& Zhang, Huihui…[et al.]. 2020. Deep Learning on Computational-Resource-Limited Platforms: A Survey. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1192480
Modern Language Association (MLA)
Chen, Chunlei…[et al.]. Deep Learning on Computational-Resource-Limited Platforms: A Survey. Mobile Information Systems No. 2020 (2020), pp.1-19.
https://search.emarefa.net/detail/BIM-1192480
American Medical Association (AMA)
Chen, Chunlei& Zhang, Peng& Zhang, Huixiang& Dai, Jiangyan& Yi, Yugen& Zhang, Huihui…[et al.]. Deep Learning on Computational-Resource-Limited Platforms: A Survey. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1192480
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192480