IVGCVSW-2739 Updating the Readme.md and ContributorGuide.md files to add references and links to the new MLPlatform.org website (the home for the machine learning platform

Change-Id: I14819d71157ea32bb911f0a20ece3e5d2d59f64b
Signed-off-by: Saoirse Stewart <saoirse.stewart@arm.com>

2 files changed, 84 insertions, 88 deletions

diff --git a/ContributorGuide.md b/ContributorGuide.md
index 9ec1704aa0..1acd6491ff 100644
--- a/ContributorGuide.md
+++ b/ContributorGuide.md
@@ -1,39 +1,36 @@
-# Contributor Guide
-
-The ArmNN project is open for external contributors and welcomes contributions. ArmNN is licensed
-under the [MIT license](https://spdx.org/licenses/MIT.html) and all accepted contributions must have
-the same license.
-
-## Developer Certificate of Origin (DCO)
-
-Before the ArmNN project accepts your contribution, you need to certify its origin and give us your permission.  To manage this process we use Developer Certificate of Origin (DCO) V1.1 (https://developercertificate.org/).
-
-To indicate that you agree to the the terms of the DCO, you "sign off" your contribution by adding a line with your name and e-mail address to every git commit message:
-
-Signed-off-by: John Doe <john.doe@example.org>
-
-You must use your real name, no pseudonyms or anonymous contributions are accepted.
-
-## Releases
-
-Official ArmNN releases are published through the official [ArmNN Github repository](https://github.com/ARM-software/armnn).
-
-## Development repository
-
-The ArmNN development repository is hosted on the [mlplatform.org git repository](https://git.mlplatform.org/ml/armnn.git/) hosted by [Linaro](https://www.linaro.org/).
-
-## Code reviews
-
-Contributions must go through code review. Code reviews are performed through the [mlplatform.org Gerrit server](https://review.mlplatform.org). Contributors need to signup to this Gerrit server with their GitHub account
-credentials.
-
-Only reviewed contributions can go to the master branch of ArmNN.
-
-## Continuous integration
-
-Contributions to ArmNN go through testing at the Arm CI system. All unit, integration and regression
-tests must pass before a contribution gets merged to the ArmNN master branch.
-
-## Communications
-
-We encourage all ArmNN developer to subscribe to the [ArmNN developer mailing list](https://lists.linaro.org/mailman/listinfo/armnn-dev).
+# Contributor Guide

+

+The Arm NN project is open for external contributors and welcomes contributions. Arm NN is licensed under the [MIT license](https://spdx.org/licenses/MIT.html) and all accepted contributions must have the same license. For more details on contributing to Arm NN see the [Contributing page](https://mlplatform.org/contributing/) on the [MLPlatform.org](https://mlplatform.org/) website.

+

+## Developer Certificate of Origin (DCO)

+

+Before the Arm NN project accepts your contribution, you need to certify its origin and give us your permission.  To manage this process we use Developer Certificate of Origin (DCO) V1.1 (https://developercertificate.org/).

+

+To indicate that you agree to the the terms of the DCO, you "sign off" your contribution by adding a line with your name and e-mail address to every git commit message:

+

+Signed-off-by: John Doe <john.doe@example.org>

+

+You must use your real name, no pseudonyms or anonymous contributions are accepted.

+

+## Releases

+

+Official Arm NN releases are published through the official [Arm NN Github repository](https://github.com/ARM-software/armnn).

+

+## Development repository

+

+The Arm NN development repository is hosted on the [mlplatform.org git repository](https://git.mlplatform.org/ml/armnn.git/) hosted by [Linaro](https://www.linaro.org/).

+

+## Code reviews

+

+Contributions must go through code review. Code reviews are performed through the [mlplatform.org Gerrit server](https://review.mlplatform.org). Contributors need to signup to this Gerrit server with their GitHub account

+credentials.

+

+Only reviewed contributions can go to the master branch of Arm NN.

+

+## Continuous integration

+

+Contributions to Arm NN go through testing at the Arm CI system. All unit, integration and regression tests must pass before a contribution gets merged to the Arm NN master branch.

+

+## Communications

+

+We encourage all Arm NN developers to subscribe to the [Arm NN developer mailing list](https://lists.linaro.org/mailman/listinfo/armnn-dev).

diff --git a/README.md b/README.md
index 0e965fb833..c276b2ab33 100644
--- a/README.md
+++ b/README.md
@@ -1,49 +1,48 @@
-# Arm NN
-
-For more information about Arm NN, see: <https://developer.arm.com/products/processors/machine-learning/arm-nn>
-
-There is a getting started guide here using TensorFlow: <https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/configuring-the-arm-nn-sdk-build-environment-for-tensorflow>
-
-There is a getting started guide here using TensorFlow Lite: <https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/configuring-the-arm-nn-sdk-build-environment-for-tensorflow-lite>
-
-There is a getting started guide here using Caffe: <https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/configuring-the-arm-nn-sdk-build-environment-for-caffe>
-
-There is a getting started guide here using ONNX: <https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/configuring-the-arm-nn-sdk-build-environment-for-onnx>
-
-There is a guide for backend development: [Backend development guide](src/backends/README.md)
-
-### Build Instructions
-
-Arm tests the build system of Arm NN with the following build environments:
-
-* Android NDK: [How to use Android NDK to build ArmNN](BuildGuideAndroidNDK.md)
-* Cross compilation from x86_64 Ubuntu to arm64 Linux: [ArmNN Cross Compilation](BuildGuideCrossCompilation.md)
-* Native compilation under arm64 Debian 9
-
-Arm NN is written using portable C++14 and the build system uses [CMake](https://cmake.org/) so it is possible to build for a wide variety of target platforms, from a wide variety of host environments.
-
-The armnn/tests directory contains tests used during ArmNN development. Many of them depend on third-party IP, model protobufs and image files not distributed with ArmNN. The dependencies of some of the tests are available freely on the Internet, for those who wish to experiment.
-
-The 'armnn/samples' directory contains SimpleSample.cpp. A very basic example of the ArmNN SDK API in use.
-
-The 'ExecuteNetwork' program, in armnn/tests/ExecuteNetwork, has no additional dependencies beyond those required by ArmNN and the model parsers. It takes any model and any input tensor, and simply prints out the output tensor. Run with no arguments to see command-line help.
-
-The 'ArmnnConverter' program, in armnn/src/ArmnnConverter, has no additional dependencies beyond those required by ArmNN and the model parsers. It takes a model in TensorFlow format and produce a serialized model in ArmNN format. Run with no arguments to see command-line help. Note that this program can only convert models for which all operations are supported by the serialization tool (src/armnnSerializer).
-
-Note that Arm NN needs to be built against a particular version of ARM's Compute Library. The get_compute_library.sh in the scripts subdirectory will clone the compute library from the review.mlplatform.org github repository into a directory alongside armnn named 'clframework' and checkouts the correct revision
-
-### License
-
-Arm NN is provided under the [MIT](https://spdx.org/licenses/MIT.html) license.
-See [LICENSE](LICENSE) for more information. Contributions to this project are accepted under the same license.
-
-Individual files contain the following tag instead of the full license text.
-
-    SPDX-License-Identifier: MIT
-
-This enables machine processing of license information based on the SPDX License Identifiers that are available here: http://spdx.org/licenses/
-
-### Contributions
-
-The ArmNN project welcomes contributions. Please see the [Contributor Guide](ContributorGuide.md) for
-more details.
+# Arm NN

+

+Arm NN is a key component of the [machine learning platform](https://mlplatform.org/>) which is part of the [Linaro Machine Intelligence Initiative](https://www.linaro.org/news/linaro-announces-launch-of-machine-intelligence-initiative/). For more information on the machine learning platform and Arm NN, see: <https://mlplatform.org/>, also there is further Arm NN information available from <https://developer.arm.com/products/processors/machine-learning/arm-nn>

+

+There is a getting started guide here using TensorFlow: <https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/configuring-the-arm-nn-sdk-build-environment-for-tensorflow>

+

+There is a getting started guide here using TensorFlow Lite: <https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/configuring-the-arm-nn-sdk-build-environment-for-tensorflow-lite>

+

+There is a getting started guide here using Caffe: <https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/configuring-the-arm-nn-sdk-build-environment-for-caffe>

+

+There is a getting started guide here using ONNX: <https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/configuring-the-arm-nn-sdk-build-environment-for-onnx>

+

+There is a guide for backend development: [Backend development guide](src/backends/README.md)

+

+### Build Instructions

+

+Arm tests the build system of Arm NN with the following build environments:

+

+* Android NDK: [How to use Android NDK to build Arm NN](BuildGuideAndroidNDK.md)

+* Cross compilation from x86_64 Ubuntu to arm64 Linux: [Arm NN Cross Compilation](BuildGuideCrossCompilation.md)

+* Native compilation under arm64 Debian 9

+

+Arm NN is written using portable C++14 and the build system uses [CMake](https://cmake.org/) so it is possible to build for a wide variety of target platforms, from a wide variety of host environments.

+

+The armnn/tests directory contains tests used during Arm NN development. Many of them depend on third-party IP, model protobufs and image files not distributed with Arm NN. The dependencies of some of the tests are available freely on the Internet, for those who wish to experiment.

+

+The 'armnn/samples' directory contains SimpleSample.cpp. A very basic example of the ArmNN SDK API in use.

+

+The 'ExecuteNetwork' program, in armnn/tests/ExecuteNetwork, has no additional dependencies beyond those required by Arm NN and the model parsers. It takes any model and any input tensor, and simply prints out the output tensor. Run with no arguments to see command-line help.

+

+The 'ArmnnConverter' program, in armnn/src/ArmnnConverter, has no additional dependencies beyond those required by Arm NN and the model parsers. It takes a model in TensorFlow format and produces a serialized model in Arm NN format. Run with no arguments to see command-line help. Note that this program can only convert models for which all operations are supported by the serialization tool (src/armnnSerializer).

+

+Note that Arm NN needs to be built against a particular version of ARM's Compute Library. The get_compute_library.sh in the scripts subdirectory will clone the compute library from the review.mlplatform.org github repository into a directory alongside armnn named 'clframework' and checkouts the correct revision

+

+### License

+

+Arm NN is provided under the [MIT](https://spdx.org/licenses/MIT.html) license.

+See [LICENSE](LICENSE) for more information. Contributions to this project are accepted under the same license.

+

+Individual files contain the following tag instead of the full license text.

+

+    SPDX-License-Identifier: MIT

+

+This enables machine processing of license information based on the SPDX License Identifiers that are available here: http://spdx.org/licenses/

+

+### 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).