Incremental and parallel proximal SVM algorithm tailored on the Jetson Nano for the ImageNet challenge
International Journal of Web Information Systems
ISSN: 1744-0084
Article publication date: 22 July 2022
Issue publication date: 25 October 2022
Abstract
Purpose
This paper aims to propose the new incremental and parallel training algorithm of proximal support vector machines (Inc-Par-PSVM) tailored on the edge device (i.e. the Jetson Nano) to handle the large-scale ImageNet challenging problem.
Design/methodology/approach
The Inc-Par-PSVM trains in the incremental and parallel manner ensemble binary PSVM classifiers used for the One-Versus-All multiclass strategy on the Jetson Nano. The binary PSVM model is the average in bagged binary PSVM models built in undersampling training data block.
Findings
The empirical test results on the ImageNet data set show that the Inc-Par-PSVM algorithm with the Jetson Nano (Quad-core ARM A57 @ 1.43 GHz, 128-core NVIDIA Maxwell architecture-based graphics processing unit, 4 GB RAM) is faster and more accurate than the state-of-the-art linear SVM algorithm run on a PC [Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32 GB RAM].
Originality/value
The new incremental and parallel PSVM algorithm tailored on the Jetson Nano is able to efficiently handle the large-scale ImageNet challenge with 1.2 million images and 1,000 classes.
Keywords
Acknowledgements
This work received support from the College of Information Technology, Can Tho University. The author would like to thank the Big Data and Mobile Computing Laboratory.
Citation
Do, T.-N. (2022), "Incremental and parallel proximal SVM algorithm tailored on the Jetson Nano for the ImageNet challenge", International Journal of Web Information Systems, Vol. 18 No. 2/3, pp. 137-155. https://doi.org/10.1108/IJWIS-03-2022-0055
Publisher
:Emerald Publishing Limited
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