Publishing 2019 R1 content

[platform/upstream/dldt.git] / inference-engine / thirdparty / mkl-dnn / doc / mainpage.md

diff --git a/inference-engine/thirdparty/mkl-dnn/doc/mainpage.md b/inference-engine/thirdparty/mkl-dnn/doc/mainpage.md
index 2a0c7a8..fdec549 100644 (file)
--- a/inference-engine/thirdparty/mkl-dnn/doc/mainpage.md
+++ b/inference-engine/thirdparty/mkl-dnn/doc/mainpage.md
@@ -26,9 +26,9 @@ The table below summarizes the list of supported functions and their variants.
 |                   | 3D direct deconvolution  | x             | x              |                |
 | Inner Product     | 2D inner product         | x             | x              | x              |
 |                   | 3D inner product         | x             | x              |                |
-| RNN (experimental)| Vanilla RNN cell         | x             | x              |                |
-|                   | LSTM cell                | x             | x              |                |
-|                   | GRU cell                 | x             | x              |                |
+| RNN               | Vanilla RNN              | x             | x              |                |
+|                   | LSTM                     | x             | x              | x              |
+|                   | GRU                      | x             | x              |                |
 | Pooling           | 2D maximum pooling       | x             | x              | x              |
 |                   | 2D average pooling       | x             | x              | x              |
 |                   | 3D maximum pooling       | x             | x              |                |
@@ -36,19 +36,22 @@ The table below summarizes the list of supported functions and their variants.
 | Normalization     | 2D LRN (within channel)  | x             | x              |                |
 |                   | 2D LRN (across channels) | x             | x              |                |
 |                   | 2D batch normalization   | x             | x              |                |
-|                   | 3D Batch Normalization   | x             | x              |                |
-| Activation        | ReLU                     | x             | x              | x              |
-|                   | Tanh                     |               | x              |                |
-|                   | ELU                      |               | x              |                |
-|                   | Bounded ReLU             |               | x              |                |
-|                   | Soft ReLU                |               | x              |                |
-|                   | Logistic regression      |               | x              |                |
+|                   | 3D batch normalization   | x             | x              |                |
+| Activation and    | ReLU                     | x             | x              | x              |
+| elementwise       | Tanh                     | x             | x              |                |
+| functions         | ELU                      | x             | x              |                |
+|                   | Square                   | x             | x              |                |
+|                   | Sqrt                     | x             | x              |                |
+|                   | Abs                      | x             | x              |                |
+|                   | Linear                   | x             | x              |                |
+|                   | Bounded ReLU             | x             | x              |                |
+|                   | Soft ReLU                | x             | x              |                |
+|                   | Logistic                 | x             | x              |                |
 |                   | Softmax                  | x             | x              |                |
 | Data manipulation | Reorder/quantization     | x             | x              | x              |
 |                   | Sum                      | x             | x              | x              |
 |                   | Concat                   | x             | x              | x              |
-|                   | Elementwise operations   |               | x              |                |
-|                   | Channel Shuffle          | x             | x              | x              |
+|                   | Shuffle                  | x             | x              | x              |
 
 ## Programming Model
 
@@ -140,7 +143,7 @@ The following examples are available in the /examples directory and provide more
     - C: simple_training.c
     - C++: simple_training_net.cpp
 
-* Creation of forward propagation of GNMT topology (experimental support)
+* Creation of forward propagation of GNMT topology
     - C++: simple_rnn.cpp
 
 * Training RNN with sequences of variable length
@@ -152,6 +155,7 @@ The following examples are available in the /examples directory and provide more
    format `any` for input or output.
    The memory format chosen is based on different circumstances such as hardware and
    convolutional parameters.
+*  Convolution could be executed using the [Winograd algorithm](@ref winograd_convolution) for a significant performance boost.
 *  Operation primitives (such as ReLU, LRN, or pooling) following convolution or
    inner product, should have input in the same memory format as the
    convolution or inner-product. Reordering can be an expensive
@@ -162,6 +166,7 @@ The following examples are available in the /examples directory and provide more
    might need workspace memory for storing results of intermediate operations
    that help with backward propagation.
 
+
 The following link provides a guide to MKLDNN verbose mode for profiling execution:
 
 * [Performance profiling](@ref perf_profile)