Optimization of Deep Learning Algorithms for Object Classification

被引：0
|
作者：
Horvath, Andras [1 ]
机构：
[1] Pazmany Peter Catholic Univ, Fac Informat Technol & Bion, Budapest, Hungary
来源：
EIGHTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2016) | 2017年 / 10225卷
关键词：
Deep learning; Convolutional Neural Networks; Embedded systems; Linearization;
D O I：
10.1117/12.2266403
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
Deep learning is currently the state of the art algorithm for image classification. The complexity of these feed-forward neural networks have overcome a critical point, resulting algorithmic breakthroughs in various fields. On the other hand their complexity makes them executable in tasks, where High-throughput computing powers are available. The optimization of these networks -considering computational complexity and applicability on embedded systems- has not yet been studied and investigated in details. In this paper I show some examples how this algorithms can be optimized and accelerated on embedded systems.
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页数：5
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