diff --git a/doc/v2/design/mkl/mkldnn.md b/doc/v2/design/mkl/mkldnn.md
index 1bd2e7bc34ee79eb753b3520d97e5e7beca89b0b..bd5bcf6f67168c21cebb046a629b948d1661e75c 100644
--- a/doc/v2/design/mkl/mkldnn.md
+++ b/doc/v2/design/mkl/mkldnn.md
@@ -5,7 +5,7 @@
充分展现英特尔平台的优势,有效提升PaddlePaddle在英特尔架构上的性能。
-
+
Figure 1. PaddlePaddle on IA
@@ -42,16 +42,43 @@ Figure 1. PaddlePaddle on IA
MKL,MKLML以及MKL-DNN三者关系如下表:
-| Name | Open Source | License | Descriptions |
-| :---------- | :--------------- | :---------- | :------------ |
-| MKL | No | Proprietary | Accelerate math processing routines |
-| MKLML | No | Proprietary | Small package of MKL, especially for Machine Learning |
-| MKL-DNN | Yes | Apache 2.0 | Accelerate primitives processing routines especially for Deep Neural Networks |
+
+
+
+Name |
+Open Source |
+License |
+Descriptions |
+
+
+
+
+MKL |
+No |
+Proprietary |
+Accelerate math processing routines |
+
+
+MKLML |
+No |
+Proprietary |
+Small package of MKL, especially for Machine Learning |
+
+
+
+MKL-DNN |
+Yes |
+Apache 2.0 |
+Accelerate primitives processing routines especially for Deep Neural Networks |
+
+
+
+
MKLML可以与MKL-DNN共同使用,以此达到最好的性能。
-
+
Figure 2. PaddlePaddle with MKL Engines
@@ -103,7 +130,7 @@ MKL-DNN的库目前只有动态库`libmkldnn.so`。
所以我们定义了一个`MKLDNNMatrix`用于管理MKL-DNN数据的不同格式以及相互之间的转换。
-
+
Figure 3. MKLDNNMatrix
@@ -113,7 +140,7 @@ Figure 3. MKLDNNMatrix
子类只需要使用定义好的接口,实现具体的函数功能即可。
-
+
Figure 4. MKLDNNLayer
@@ -150,7 +177,7 @@ Figure 4. MKLDNNLayer
所以整体上,在实现每个子类的时候就不需要关心分支的事情了。
-
+
Figure 5. Merge Gradients
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new file mode 100644
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+digraph G{
+ subgraph cluster_timestep0 {
+ label="recurrent timestep i-1"
+ bgcolor=lightgray
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+ fc0_2 [label="fc 2"]
+
+ fc0_0 -> fc0_1
+ fc0_1 -> fc0_2
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+
+ subgraph cluster_timestep1 {
+ label="recurrent timestep i"
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+ color=blue
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+ subgraph cluster_timestep2 {
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+ bgcolor=lightgray
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new file mode 100644
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--- /dev/null
+++ b/doc/v2/images/glossary_rnn_with_memory.dot
@@ -0,0 +1,48 @@
+digraph G{
+ subgraph cluster_timestep0 {
+ label="recurrent timestep i-1"
+ bgcolor=lightgray
+ node [style=filled,color=white]
+ fc0_0 [label="fc 0"]
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+ fc0_0 -> fc0_1
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+ }
+
+ subgraph cluster_timestep1 {
+ label="recurrent timestep i"
+ node [style=filled];
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+ color=blue
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+ fc1_1 -> fc1_2
+ fc1_1 -> m1
+ m1 -> fc1_1
+ }
+
+ subgraph cluster_timestep2 {
+ label="recurrent timestep i+1"
+ bgcolor=lightgray
+ node [style=filled,color=white]
+ fc2_0 [label="fc 0"]
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+ fc2_0 -> fc2_1
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+ fc2_1 -> m2
+ m2 -> fc2_1
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+
+
+ m0 -> m1 [style="dotted" constraint=false]
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+ a -> b;
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