Neural network model compiler for Arm Ethos-U NPUs

# 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 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.

## TensorFlow Support

Vela supports TensorFlow 2.3.0

## 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)

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

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


## 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. Compile a network using a particular Ethos-U NPU. The following command selects an Ethos-U65 NPU accelerator configured with 512 MAC units.
vela --accelerator-config ethos-u65-512 my_model.tflite

1. Compile a network using a particular embedded system configuration defined in Vela's configuration file. The following command selects the My_Sys_Config system configuration along with the My_Mem_Mode memory mode from the vela_cfg.ini configuration file.
vela --config vela_cfg.ini --system-config My_Sys_Config --memory-mode My_Mem_Mode 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.

## External APIs

Vela provides a low-level external API to enable Ethos-U code generation from other tools. Please see Vela External APIs.

## Example Networks

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

## Supported Operators

Please see Supported Operators for the list of supported operators in this release.

## Project details

Uploaded source