<p dir="auto">cuDNN v6.0 was released on Mar 23, are there any plans for adding compatibility (building against compatibility version 6000)?</p>
<p dir="auto">cuDNN v6.0 has been released. There are some cool new features:</p>
<ul dir="auto">
<li>Dilated Convolutions: Dilated Convolutions are now supported in cuDNN without a<br>
change in API.</li>
<li><code class="notranslate">cudnnConvolutionBiasActivationForward</code> allows for the execution of a single kernel fusing convolution, bias and activation operations</li>
</ul>
<p dir="auto">Full release notes:</p>
<blockquote>
<p dir="auto">Dilated Convolutions: Dilated Convolutions are now supported in cuDNN without a<br>
change in API. Previously unused “upscale” fields in the Convolution Descriptor<br>
have been repurposed to allow user specification of dilation factors along each<br>
dimension. Support for dilation is present in the following code paths :<br>
Forward : CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM,<br>
Backward Data: CUDNN_CONVOLUTION_BWD_DATA_ALGO_0 and<br>
Backward Filter: CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0<br>
 The new API cudnnConvolutionBiasActivationForward allows for the execution of<br>
a single kernel fusing convolution, bias and activation operations. At present, only<br>
per channel bias and RELU activation are supported.<br>
 Inference on 8 bit integer data is now supported, leveraging the 4 element dot<br>
product instruction (IDP4A) of Pascal GPUs with CUDA capabilities 6.1. Two tensor<br>
layouts are supported for this feature: CUDNN_TENSOR_NHWC with INT8 data<br>
type and CUDNN_TENSOR_NCHW_VECT with INT8x4 data type.<br>
 RNN now supports 3 algorithms<br>
o CUDNN_RNN_ALGO_STANDARD :<br>
 Same functionality as in CUDNN v5.1<br>
o CUDNN_RNN_ALGO_PERSIST_STATIC :<br>
 This algorithm relies on the usage of persistent CUDA kernels which<br>
are pre-compiled to fit different GPUs.<br>
 This algorithm is available only on Pascal GPUs.<br>
o CUDNN_RNN_ALGO_PERSIST_DYNAMIC :<br>
This algorithm also relies on the usage of persistent CUDA kernels<br>
but these kernels are compiled at runtime using nvrtc. In some cases<br>
this results in a significant performance benefit.<br>
 This algorithm is also available only on Pascal GPUs and is supported<br>
only on Linux and Windows.<br>
 Support for 1D-FFT convolutions has been added<br>
 New API routine cudnnReduceTensor has been added, supporting 8 reduction<br>
operations<br>
 Activation mode CUDNN_ACTIVATION_ELU is now supported.<br>
 A deterministic max pooling mode CUDNN_POOLING_MAX_DETERMINISTIC<br>
has been added.<br>
 Significant performance improvement for softmax layers for mode<br>
CUDNN_SOFTMAX_MODE_CHANNEL has been achieved when low batch<br>
number is used.<br>
 Significant performance improvements have been added for cudnnAddTensor<br>
when spatial dimensions are set to 1.</p>
</blockquote>