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Optimise TensorFlow Lite models for Ethos-U55 NPU.

Project description

Vela

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 Ethos-U55 NPU.

The optimised model will contain TensorFlow Lite Custom operators for those parts of the model that can be accelerated by the Ethos-U55. 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-U55 NPU embedded system.

The tool will also generate performance estimates (EXPERIMENTAL) for the compiled model.

TensorFlow Support

Vela supports TensorFlow 2.1.0 (for experimental Int16 support please use the latest nightly build of TensorFlow).

Environment

Vela runs on the Linux operating system.

Prerequisites

The following should be installed prior to the installation of Vela:

  • Python >= 3.6
  • Pip3
  • GNU toolchain (GCC, Binutils and libraries) or alternative C compiler/linker toolchain

And optionally:

  • Pipenv virtual environment tool

Installation

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.

PyPi

Install Vela from PyPi using the following command:

pip3 install ethos-u-vela

ML Platform

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 .

Advanced Installation for Developers

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).

Running

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:

  1. Compile the network my_model.tflite. The optimised version will be output to ./output/my_network_vela.tflite.
vela my_model.tflite
  1. Compile the network /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
  1. To specify information about the embedded system's configuration use Vela's system configuration file. The following command selects the 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
  1. To get a list of all available options:
vela --help

Information about all of Vela's CLI options as well as the system configuration file format can be found in Vela Options.

Testing

Please see Vela Testing

Contributions

Please see Vela Contributions.

Security

Please see Vela Security.

Releases

Please see Vela Releases.

Resources

Additional useful information:

License

Vela is licensed under Apache License 2.0

Project details


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ethos-u-vela-1.1.0.tar.gz (162.4 kB view hashes)

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