Complexity of Deep Convolutional Neural Networks in Mobile Computing

Neural networks employ massive interconnection of simple computing units called neurons to compute the problems that are highly nonlinear and could not be hard coded into a program. These neural networks are computation-intensive, and training them requires a lot of training data. Each training exam...

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Veröffentlicht in:Complexity (New York, N.Y.) Jg. 2020; H. 2020; S. 1 - 8
Hauptverfasser: Nazir, Shah, Khan, Habib Ullah, Jamil, Noreen, Naeem, Saad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 17.09.2020
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John Wiley & Sons, Inc
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ISSN:1076-2787, 1099-0526
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Abstract Neural networks employ massive interconnection of simple computing units called neurons to compute the problems that are highly nonlinear and could not be hard coded into a program. These neural networks are computation-intensive, and training them requires a lot of training data. Each training example requires heavy computations. We look at different ways in which we can reduce the heavy computation requirement and possibly make them work on mobile devices. In this paper, we survey various techniques that can be matched and combined in order to improve the training time of neural networks. Additionally, we also review some extra recommendations to make the process work for mobile devices as well. We finally survey deep compression technique that tries to solve the problem by network pruning, quantization, and encoding the network weights. Deep compression reduces the time required for training the network by first pruning the irrelevant connections, i.e., the pruning stage, which is then followed by quantizing the network weights via choosing centroids for each layer. Finally, at the third stage, it employs Huffman encoding algorithm to deal with the storage issue of the remaining weights.
AbstractList Neural networks employ massive interconnection of simple computing units called neurons to compute the problems that are highly nonlinear and could not be hard coded into a program. These neural networks are computation-intensive, and training them requires a lot of training data. Each training example requires heavy computations. We look at different ways in which we can reduce the heavy computation requirement and possibly make them work on mobile devices. In this paper, we survey various techniques that can be matched and combined in order to improve the training time of neural networks. Additionally, we also review some extra recommendations to make the process work for mobile devices as well. We finally survey deep compression technique that tries to solve the problem by network pruning, quantization, and encoding the network weights. Deep compression reduces the time required for training the network by first pruning the irrelevant connections, i.e., the pruning stage, which is then followed by quantizing the network weights via choosing centroids for each layer. Finally, at the third stage, it employs Huffman encoding algorithm to deal with the storage issue of the remaining weights.
Audience Academic
Author Nazir, Shah
Naeem, Saad
Khan, Habib Ullah
Jamil, Noreen
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CitedBy_id crossref_primary_10_1016_j_procs_2022_12_023
crossref_primary_10_1155_2022_4016073
Cites_doi 10.1109/T-C.1974.223784
10.1155/2020/8856801
10.1016/j.future.2016.09.015n
10.1109/TCSVT.2019.2910119
10.1109/proc.1969.6869
ContentType Journal Article
Copyright Copyright © 2020 Saad Naeem et al.
COPYRIGHT 2020 John Wiley & Sons, Inc.
Copyright © 2020 Saad Naeem et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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Snippet Neural networks employ massive interconnection of simple computing units called neurons to compute the problems that are highly nonlinear and could not be hard...
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SubjectTerms Accuracy
Algorithms
Artificial neural networks
Brain
Centroids
Counting
Electronic devices
Huffman codes
Knowledge
Learning
Mobile computing
Mobile devices
Neural networks
Neurons
Pattern recognition
Signal processing
Surveys
Time compression
Training
Video compression
Wireless networks
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