This tool is used to compile a TensorFlow Lite for Microcontrollers neural network model into an optimised version that can run on an embedded system containing an Arm Ethos-U NPU.
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.
Vela supports TensorFlow 2.3.0
Vela runs on the Linux operating system.
The following should be installed prior to the installation of Vela:
And optionally:
Vela is available to install as a package from PyPi, or as source code from ML Platform. Both methods will automatically install all the required dependencies.
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:
https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela
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:
pip3 install -U setuptools>=40.1.0 pip3 install .
Or, if you use pipenv
:
pipenv 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 above install commands like so:
pip3 install -e .
Or, if you use pipenv
:
pipenv install -e .
If you plan to contribute to the Vela project (highly encouraged!) then it is recommended to install Vela along with the pre-commit tools (see Vela Testing for more details).
mlw_codec
As part of the installation process, Vela will compile a C based module.
The build flags used for this module are as follows:
-Wall -Werror -Wno-unused-function -Wno-unused-variable
Vela is run with an input .tflite
file passed on the command line. This file contains the neural network to be compiled. The tool then outputs an optimised version with a _vela.tflite
file prefix, along with the performance estimate (EXPERIMENTAL) CSV files, all to the output directory.
If you use the pipenv
virtual environment tool then first start by spawning a shell in the virtual environment:
pipenv shell
After which running Vela is the same regardless of whether you are in a virtual environment or not.
Example usage:
my_model.tflite
. The optimised version will be output to ./output/my_network_vela.tflite
.vela my_model.tflite
/path/to/my_model.tflite
and specify the output to go in the directory ./results_dir/
.vela --output-dir ./results_dir /path/to/my_model.tflite
MySysConfig
settings that are described in the sys_cfg_vela.ini
system configuration file. More details can be found in the next section.vela --config sys_cfg_vela.ini --system-config MySysConfig my_model.tflite
vela --help
Information about all of Vela's CLI options as well as the system configuration file format can be found in Vela Options.
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
Please see Vela Testing.
Please see Vela Contributions.
Please see Vela Security.
Please see Vela Releases.
Additional useful information:
Vela is licensed under Apache License 2.0.