IVGCVSW-4792 Update armnn readme and support files

 * Add TPIP used by Arm NN
 * Add SqueezeNet to CaffeSupport.md
 * Add ResNet v2 50 to TensorFlowSupport.md
 * Update tested networks in TensorFlowLiteSupported.md

Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Change-Id: I15927ea600a3dfffee082933d32f235f3940730e

4 files changed, 26 insertions, 0 deletions

diff --git a/README.md b/README.md
index 25dab0edf9..8766d3f184 100644
--- a/README.md
+++ b/README.md
@@ -50,6 +50,13 @@ Individual files contain the following tag instead of the full license text.
 
 This enables machine processing of license information based on the SPDX License Identifiers that are available here: http://spdx.org/licenses/
 
+TPIP used by Arm NN:
+
+| Name | License (SPDX ID) |
+|------|-------------------|
+| half | MIT               |
+| stb  | MIT               |
+
 ### Contributions
 
 The Arm NN project welcomes contributions. For more details on contributing to Arm NN see the [Contributing page](https://mlplatform.org/contributing/) on the [MLPlatform.org](https://mlplatform.org/) website, or see the [Contributor Guide](ContributorGuide.md).
diff --git a/src/armnnCaffeParser/CaffeSupport.md b/src/armnnCaffeParser/CaffeSupport.md
index 5d998937de..ab0d3a4ab8 100644
--- a/src/armnnCaffeParser/CaffeSupport.md
+++ b/src/armnnCaffeParser/CaffeSupport.md
@@ -11,6 +11,7 @@ Although some other neural networks might work, Arm tests the Arm NN SDK with Ca
 - Yolov1_tiny.
 - Lenet.
 - MobileNetv1.
+- SqueezeNet v1.0 and SqueezeNet v1.1
 
 The Arm NN SDK supports the following machine learning layers for Caffe networks:
 
diff --git a/src/armnnTfLiteParser/TensorFlowLiteSupport.md b/src/armnnTfLiteParser/TensorFlowLiteSupport.md
index 88db0f07b2..f7828628cb 100644
--- a/src/armnnTfLiteParser/TensorFlowLiteSupport.md
+++ b/src/armnnTfLiteParser/TensorFlowLiteSupport.md
@@ -94,4 +94,20 @@ Arm tested these operators with the following TensorFlow Lite neural network:
 
 * DeepSpeaker
 
+* [DeepLab v3+](https://www.tensorflow.org/lite/models/segmentation/overview)
+
+* FSRCNN
+
+* RDN converted from [TensorFlow model](https://github.com/hengchuan/RDN-TensorFlow)
+
+* Quantized RDN (CpuRef)
+
+* [Quantized Inception v3](http://download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz)
+
+* [Quantized Inception v4](http://download.tensorflow.org/models/inception_v4_299_quant_20181026.tgz) (CpuRef)
+
+* Quantized ResNet v2 50 (CpuRef)
+
+* Quantized Yolo v3 (CpuRef)
+
 More machine learning operators will be supported in future releases.
diff --git a/src/armnnTfParser/TensorFlowSupport.md b/src/armnnTfParser/TensorFlowSupport.md
index 65839e2e57..2dc54b5f85 100644
--- a/src/armnnTfParser/TensorFlowSupport.md
+++ b/src/armnnTfParser/TensorFlowSupport.md
@@ -189,4 +189,6 @@ Arm tests these operators with the following TensorFlow fp32 neural networks:
 
 * inception_v3. The Arm NN SDK only supports the official inception_v3 transformed model. See the TensorFlow documentation on [preparing models for mobile deployment](https://www.tensorflow.org/mobile/prepare_models) for more information on how to transform the inception_v3 network.
 
+* ResNet v2 50 implementation from the [TF Slim model zoo](https://github.com/tensorflow/models/tree/master/research/slim)
+
 More machine learning operators will be supported in future releases.