Fast Computing Framework for Convolutional Neural Networks

被引：1

作者：

Korytkowski, Marcin ^{[1
]}

Staszewski, Pawel ^{[1
]}

Woldan, Piotr ^{[1
]}

Scherer, Rafal ^{[1
]}

机构：

[1] Czestochowa Tech Univ, Inst Computat Intelligence, Comp Vis & Data Min Lab, Al Armii Krajowej 36, PL-42200 Czestochowa, Poland

来源：

PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCES ON BIG DATA AND CLOUD COMPUTING (BDCLOUD 2016) SOCIAL COMPUTING AND NETWORKING (SOCIALCOM 2016) SUSTAINABLE COMPUTING AND COMMUNICATIONS (SUSTAINCOM 2016) (BDCLOUD-SOCIALCOM-SUSTAINCOM 2016) | 2016年

关键词：

IMAGE; SEGMENTATION; EDGE;

D O I：

10.1109/BDCloud-SocialCom-SustainCom.2016.28

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the case of building large convolutional neural networks, signal propagation speed is one of priority factors. Training large neural structures requires enormous time for achieving satisfying accuracy. In addition, the networks need to be learn by very large sets of good quality training images, which is another time-consuming factor. The paper presents a fast computing framework with some methods to optimize the signal propagation speed. We compare our implementation with the original OverFeat implementation.

引用

页码：118 / 123

页数：6

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