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.

The tool has limited functionality for compiling a TOSA neural network (EXPERIMENTAL).

## TensorFlow Support

• Vela 3.6.0 to current supports TensorFlow 2.10
• Vela 3.5.0 supports TensorFlow 2.9
• Vela 3.4.0 supports TensorFlow 2.8
• Vela 3.3.0 supports TensorFlow 2.7
• Vela 3.1.0 to 3.2.0 supports TensorFlow 2.5
• Vela 2.1.0 to 3.0.0 supports TensorFlow 2.4
• Vela 2.0.0 to 2.0.1 supports TensorFlow 2.3
• Vela 0.1.0 to 1.2.0 supports TensorFlow 2.1

## Environment

Vela runs on Linux and Microsoft Windows 10 operating systems.

## Prerequisites

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

• Python 3.7 or compatible
• Development version containing the Python/C API header files
• e.g. apt install python3.7-dev or yum install python37-devel
• Pip3
• A C99 capable compiler and associated toolchain

## 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 from the root directory of the repository:

pip3 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 install command like so:

pip3 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 or .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.

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 targeting 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 while minimizing peak SRAM usage, prioritising lower SRAM usage over runtime performance.
vela --optimise Size my_model.tflite

1. Compile a network to have maximum performance, i.e. the fastest inference time. This prioritises a higher runtime performance over a lower peak SRAM usage.
vela --optimise Performance my_model.tflite

1. Compile a network while optimising for the fastest inference time possible, with an upper bound for the SRAM usage. The memory limit is set in bytes, i.e. run the following example if one requires a limit of 300KB.
vela --optimise Performance --arena-cache-size 300000 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.ini configuration 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

1. To get a list of all available configuration files in the config_files directory:
vela --list-config-files

1. To get a list of all available options (see CLI Options section below):
vela --help


## Warnings

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.

## 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

## APIs

Please see Vela External APIs.

## Contributions

Please see Vela Contributions.

## Debug Database

Please see Vela Debug Database.

## Options

Please see Vela CLI Options. This includes a description of the system configuration file format.

## Performance

Please see Vela Performance Estimation Summary.

## Releases

Please see Vela Releases.

## Security

Please see Vela Security.

## Supported Operators

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

## Testing

Please see Vela Testing.

## Bug Reporting

Please see Vela Community Bug Reporting for a description of how to report bugs.

## Release history Release notifications | RSS feed

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