Deep Neural Network Library (DNNL)  1.2.0
Performance library for Deep Learning
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Modules
Here is a list of all modules:
[detail level 123]
\DNNL API
 oPrimitivesCompute primitives
 |oCommonCommon operations to create, destroy and inspect primitives
 |oAttributesA container for parameters that extend primitives behavior
 |oReorderA primitive to copy data between two memory objects
 |oConcatA primitive to concatenate data by arbitrary dimension
 |oSumA primitive to sum multiple tensors
 |oBinaryA primitive to perform tensor operations over two tensors
 |oConvolutionA primitive to perform 1D, 2D or 3D convolution
 |oDeconvolutionA primitive to perform 1D, 2D or 3D deconvolution
 |oShuffleA primitive to shuffle tensor data along an axis
 |oEltwiseA primitive to perform elementwise operations such as the rectifier linear unit (ReLU)
 |oSoftmaxA primitive to perform softmax
 |oLogSoftmaxA primitive to perform logsoftmax
 |oPoolingA primitive to perform max or average pooling
 |oLRNA primitive to perform local response normalization (LRN) across or within channels
 |oBatch NormalizationA primitive to perform batch normalization
 |oLayer NormalizationA primitive to perform layer normalization
 |oInner ProductA primitive to compute an inner product
 |oRNNA primitive to compute recurrent neural network layers
 |oMatrix MultiplicationA primitive to perform matrix-matrix multiplication
 |\ResamplingA primitive to compute resampling operation on 1D, 2D or 3D data tensor using Nearest Neighbor, or Linear (Bilinear, Trilinear) interpolation method
 oMemoryA container that describes and stores data
 oEngineAn abstraction of a computational device: a CPU, a specific GPU card in the system, etc
 oStreamAn encapsulation of execution context tied to a particular engine
 oServiceA set of functions that aid in DNNL debugging and profiling
 oBLAS functionsA subset of Basic Linear ALgebra (BLAS) functions that perform matrix-matrix multiplication
 \UtilitiesUtility types and definitions