Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1959
Title: Cifar-10 Image Classification with Convolutional Neural Networks For Embedded Systems
Authors: Çalık, Rasim Caner
Demirci, Muhammed Fatih
143569
Keywords: Neural network
Convolution
Convolutional layers
Issue Date: 2018
Publisher: IEEE
Source: Çalik, R. C., & Demirci, M. F. (2018, October). Cifar-10 Image Classification with Convolutional Neural Networks for Embedded Systems. In 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) (pp. 1-2). IEEE.
Series/Report no.: Proceedings of IEEE/ACS International Conference on Computer Systems and Applications
Abstract: Convolutional Neural Networks (CNN) have been successfully applied to image classification problems.Although powerful, they require a large amount of memory. The purpose of this paper is to perform image classification using CNNs on the embedded systems, where only a limited amount of memory is available. Our experimental analysis shows that 85.9% image classification accuracy is obtained by our framework while requiring 2GB memory only, making our framework ideal to be used in embedded systems.
Description: 15th IEEE/ACS International Conference on Computer Systems and Applications (2018 : Aqaba; Jordan)
URI: https://ieeexplore.ieee.org/document/8612873
https://hdl.handle.net/20.500.11851/1959
ISBN: 978-1-5386-9120-5
ISSN: 2161-5322
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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