| commit | 7997a3527218ed821ec933ef3a5e6a3f07409b21 | [log] [tgz] |
|---|---|---|
| author | Matteo Martincigh <matteo.martincigh@arm.com> | Wed Apr 17 15:37:30 2019 +0100 |
| committer | Matteo Martincigh <matteo.martincigh@arm.com> | Thu Apr 18 08:50:28 2019 +0100 |
| tree | eeb2db8e8bdebf7f2661b68890ff6d31def7f620 | |
| parent | c2ebc63baf19ab6c3da6ae7a982c9eba0c0d85be [diff] |
IVGCVSW-2980 Build ArmNN with the latest version of the driver stack library * Changed the pre-compiled object held by the pre-compiled layer into a unique pointer, so that now the layer has the ownership of it * Changed the pre-compiled object held by the descriptor and the workload into a naked pointer, to leave the ownership to the layer Change-Id: I4a582e45ca0aa3978e8e40b786c743a6eddce852 Signed-off-by: Matteo Martincigh <matteo.martincigh@arm.com>
Arm NN is a key component of the machine learning platform which is part of the Linaro 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
Arm tests the build system of Arm NN with the following build environments:
Arm NN is written using portable C++14 and the build system uses CMake 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
Arm NN is provided under the MIT license. See 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/
The Arm NN project welcomes contributions. For more details on contributing to Arm NN see the Contributing page on the MLPlatform.org website, or see the Contributor Guide.