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Posted to dev@singa.apache.org by "wangwei (JIRA)" <ji...@apache.org> on 2015/12/29 10:28:49 UTC

[jira] [Resolved] (SINGA-100) Implement layers using CUDNN for GPU training

     [ https://issues.apache.org/jira/browse/SINGA-100?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

wangwei resolved SINGA-100.
---------------------------
    Resolution: Fixed

Resolved together with SINGA-88.

> 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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