| commit | d80a7126b0abdd532a9f731559827a23f2e565e0 | [log] [tgz] |
|---|---|---|
| author | Matthew Bentham <matthew.bentham@arm.com> | Tue Jan 08 17:52:37 2019 +0000 |
| committer | Matthew Bentham <matthew.bentham@arm.com> | Tue Jan 08 18:00:12 2019 +0000 |
| tree | e04f6454e2353469dd9806b2a589c54b61dd777d | |
| parent | a1d3c6a49f35d7d3f11cc7e1b588d1d5401bdbf1 [diff] |
Refactor: Don't include all ComputeLibrary function definitions everywhere. Just include the function definition that is specifically needed for each workload. Also, tighten up the scope where Compute Library functions are available. Knocks about 30seconds off a 4m30s single-threaded compile of the Neon workloads. Change-Id: Idac438f3bc77ff978295fbc9505cb42447def145
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
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 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 ‘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 ‘armnn/samples’ directory contains SimpleSample.cpp. A very basic example of the ArmNN SDK API in use.
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 ArmNN project welcomes contributions. Please see the Contributor Guide for more details.