Optimization of GPU Memory Usage for Training Deep Neural Networks

Che Lun Hung*, Chine fu Hsin, Hsiao Hsi Wang, Chuan Yi Tang

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Recently, Deep Neural Networks have been successfully utilized in many domains; especially in computer vision. Many famous convolutional neural networks, such as VGG, ResNet, Inception, and so forth, are used for image classification, object detection, and so forth. The architecture of these state-of-the-art neural networks has become deeper and complicated than ever. In this paper, we propose a method to solve the problem of large memory requirement in the process of training a model. The experimental result shows that the proposed algorithm is able to reduce the GPU memory significantly.

原文English
主出版物標題Pervasive Systems, Algorithms and Networks - 16th International Symposium, I-SPAN 2019, Proceedings
編輯Christian Esposito, Jiman Hong, Kim-Kwang Raymond Choo
發行者Springer
頁面289-293
頁數5
ISBN(列印)9783030301422
DOIs
出版狀態Published - 2019
事件16th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2019 - Naples, Italy
持續時間: 16 9月 201920 9月 2019

出版系列

名字Communications in Computer and Information Science
1080 CCIS
ISSN(列印)1865-0929
ISSN(電子)1865-0937

Conference

Conference16th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2019
國家/地區Italy
城市Naples
期間16/09/1920/09/19

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