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From "ASF subversion and git services (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SINGA-100) Implement layers using CUDNN for GPU training
Date Wed, 16 Dec 2015 12:11:46 GMT

    [ https://issues.apache.org/jira/browse/SINGA-100?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15059900#comment-15059900
] 

ASF subversion and git services commented on SINGA-100:
-------------------------------------------------------

Commit 8ac511c70a43c9cb5db354643b186fd4205a7104 in incubator-singa's branch refs/heads/master
from WANG Sheng
[ https://git-wip-us.apache.org/repos/asf?p=incubator-singa.git;h=8ac511c ]

SINGA-100 Implement layers using CUDNN for GPU training

check with cpplint


> Implement layers using CUDNN for GPU training
> ---------------------------------------------
>
>                 Key: SINGA-100
>                 URL: https://issues.apache.org/jira/browse/SINGA-100
>             Project: Singa
>          Issue Type: New Feature
>            Reporter: wangwei
>
> NVIDIA has released the cudnn library optimized for CNN operations like convolution,
pooling, etc. It has achieved overall good performance. Hence, it is essential to add cudnn
supported layers in SINGA for efficient GPU training (SINGA-41).
> We will use the cudnn library to implement CNN layers, namely,
>  cudnnConvolutionLayer, cudnnPoolingLayer, cudnnLRNLayer, cudnnSoftmaxLayer, cudnnReLULayer,
cudnnSigmoidLayer, cudnnTanhLayer, cudnnDivNormLayer.
> Data type float-16 will not be consider in this ticket.



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