In order to be accelerated by the Ethos-U NPU the network operators must be quantised to either 8-bit (unsigned or signed) or 16-bit (signed).
The optimised model will contain TensorFlow Lite Custom operators for those parts of the model that can be accelerated by the Ethos-U NPU. Parts of the model that cannot be accelerated are left unchanged and will instead run on the Cortex-M series CPU using an appropriate kernel (such as the Arm optimised CMSIS-NN kernels).
After compilation the optimised model can only be run on an Ethos-U NPU embedded system.
The tool will also generate performance estimates (EXPERIMENTAL) for the compiled model.
The tool has limited functionality for compiling a TOSA neural network (EXPERIMENTAL).
Vela is tested by comparing the numerical behaviour of the optimised operators against that of the corresponding TensorFlow Lite reference kernels. The following list indicates which version is used for comparison:
The majority of Vela's testing is done using a single version of Python, as indicated by the first version in the list below. However, some additional testing is also performed across a range of newer versions starting at the minimum version (pyproject.toml:project.requires-python) indicated in the brackets:
Vela runs on Linux and Microsoft Windows 10 operating systems.
The following should be installed prior to the installation of Vela:
apt install python3.10-devor
yum install python310-devel
Install Vela from PyPi using the following command:
pip3 install ethos-u-vela
First obtain the source code by either downloading the desired TGZ file from:
Or by cloning the git repository:
git clone https://review.mlplatform.org/ml/ethos-u/ethos-u-vela.git
Once you have the source code, Vela can be installed using the following command from the root directory of the repository:
pip3 install .
If you plan to modify the Vela codebase then it is recommended to install Vela as an editable package to avoid the need to re-install after every modification. This is done by adding the
-e option to the install command like so:
pip3 install -e .[dev]
If you plan to contribute to the Vela project (highly encouraged!) then it is recommended to install Vela with the development dependencies (see Vela Testing for more details).
Vela is run with an input
.tosa (EXPERIMENTAL) file passed on the command line. This file contains the neural network to be compiled. The tool then outputs an optimised
.tflite file with a
_vela suffix in the file name, along with performance estimate (EXPERIMENTAL) CSV files, all to the output directory. It also prints a performance estimation summary back to the console, see Vela Performance Estimation Summary.
my_model.tflite. The optimised version will be output to
/path/to/my_model.tfliteand specify the output to go in the directory
vela --output-dir ./results_dir /path/to/my_model.tflite
vela --accelerator-config ethos-u65-512 my_model.tflite
vela --optimise Size my_model.tflite
vela --optimise Performance my_model.tflite
vela --optimise Performance --arena-cache-size 300000 my_model.tflite
My_Sys_Configsystem configuration along with the
My_Mem_Modememory mode from the
vela.iniconfiguration file located in the config_files directory.
vela --config Arm/vela.ini --system-config My_Sys_Config --memory-mode My_Mem_Mode my_model.tflite
When running the Vela compiler it may report a number of warning messages to the console. These should all be thoroughly reviewed as they will indicate decisions that the compiler has made in order to create the optimised network.
Some example networks that contain quantised operators which can be compiled by Vela to run on the Ethos-U NPU can be found at: https://tfhub.dev/s?deployment-format=lite&q=quantized
Once ethos-u-vela is installed, the user might want to install a different NumPy version that is still within the dependency constraints defined in pyproject.toml.
In some scenarios, doing so might prevent ethos-u-vela from functioning as expected due to incompatibilities between the installed NumPy C headers used in the mlw_codec and the current version of NumPy.
In the ethos-u-vela source directory, run:
virtualenv -p 3.10 venv . venv/bin/activate pip install ethos-u-vela
Next, install a different NumPy version (e.g. 1.21.3)
pip install numpy==1.21.3 --force
Finally, run ethos-u-vela. You might get an error similar to this:
ImportError: NumPy C API version mismatch (Build-time version: 0x10, Run-time version: 0xe) This is a known issue most likely caused by a change in the API version in NumPy after installing ethos-u-vela.
In order for ethos-u-vela to work with an older version of NumPy that uses different C APIs, you will need to install the desired NumPy version first, and then build ethos-u-vela with that specific NumPy version:
Uninstall ethos-u-vela and install the desired version of NumPy
pip uninstall ethos-u-vela pip install numpy==1.21.3 --force
Install required build dependencies
pip install "setuptools_scm[toml]<6" wheel
Install ethos-u-vela without build isolation. Not using build isolation ensures that the correct version of NumPy is used when copying the C headers in mlw_codec during the build process.
pip install ethos-u-vela --no-build-isolation --no-cache-dir
Please see Vela External APIs.
Please see Vela Community Bug Reporting for a description of how to report bugs.
Please see Vela Contributions.
Please see Vela Debug Database.
This product conforms to Arm’s inclusive language policy and, to the best of our knowledge, does not contain any non-inclusive language. If you find something that concerns you, email firstname.lastname@example.org.
Please see Vela CLI Options. This includes a description of the system configuration file format.
Please see Vela Performance Estimation Summary.
Please see Vela Releases.
Additional useful information:
Please see Vela Security.
Please see Vela Supported Operators for the list of operators supported in this release.
Please see Vela Testing.
Vela is licensed under Apache License 2.0.