A Bi-layered Parallel Training Architecture for Large-Scale Convolutional Neural Networks

Benefitting from large-scale training datasets and the complex training network, Convolutional Neural Networks (CNNs) are widely applied in various fields with high accuracy. However, the training process of CNNs is very time-consuming, where large amounts of training samples and iterative operation...

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Vydané v:	IEEE transactions on parallel and distributed systems Ročník 30; číslo 5; s. 965 - 976
Hlavní autori:	Chen, Jianguo, Li, Kenli, Bilal, Kashif, Zhou, Xu, Li, Keqin, Yu, Philip S.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.05.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Acceleration Artificial neural networks bi-layered parallel computing Big data Computation Computational modeling Computer architecture Computer networks convolutional neural networks Datasets deep learning Distributed computing Distributed processing Iterative methods Neural networks Parallel processing Synchronism Task analysis Task scheduling Training Weight Weightlifting Workload
ISSN:	1045-9219, 1558-2183
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