Performance Comparison of Convolutional Neural Network Models on GPU

被引：9

作者：

Yapici, Muhammed Mutlu ^{[1
]}

Tekerek, Adem ^{[2
]}

Topaloglu, Nurettin ^{[3
]}

机构：

[1] Ankara Univ, Comp Technol Dept, Ankara, Turkey

[2] Gazi Univ, Informat Technol Dept, Ankara, Turkey

[3] Gazi Univ, Comp Engn Dept, Ankara, Turkey

来源：

2019 IEEE 13TH INTERNATIONAL CONFERENCE ON APPLICATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES (AICT 2019) | 2019年

关键词：

deep learning; VGGNet; ResNet; DenseNet; performance comparison;

D O I：

10.1109/aict47866.2019.8981749

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Deep learning methods are used in many popular areas such: image processing, computer vision, autonomous vehicles, character recognition, audio and video processing. These methods require high processing power, such as graphics cards (GPUs), to obtain successful results in the solution of NP hard problems which have big data. In this study, performance comparison of convolutional neural network (CNN) architectures were performed on GPU. ResNet, VGGNet19 and DenseNet CNN models, and GPDS signature dataset were used for comparison. According to the obtained results, ResNet50 took up the least amount of GPU memory space. The best classification results were obtained with DenseNet121 and the second one was from ResNet50.

引用

页码：164 / 167

页数：4

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