path: root/README.md

# Vela

This tool is used to compile a
[TensorFlow Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers)
neural network model into an optimised version that can run on an embedded
system containing an
[Arm Ethos-U NPU](https://www.arm.com/products/silicon-ip-cpu).

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](https://www.arm.com) optimised
[CMSIS-NN](https://github.com/ARM-software/CMSIS_5/tree/develop/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](https://git.mlplatform.org/tosa/specification.git/) neural network
(EXPERIMENTAL).

## TensorFlow Support

* Vela 3.4.0 to current 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
    - For Linux operating systems, a GNU toolchain is recommended.
    - For Microsoft Windows 10, Microsoft Visual C++ 14.2 Build Tools is recommended.
      See <https://wiki.python.org/moin/WindowsCompilers>

## Installation

Vela is available to install as a package from
[PyPi](https://pypi.org/project/ethos-u-vela/), or as
source code from
[ML Platform](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela).
Both methods will automatically install all the required dependencies.

### PyPi

Install Vela from PyPi using the following command:

```bash
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:

```bash
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:

```bash
pip3 install .
```

A `Pipfile` is maintained for the project, so users of the virtual environment
tool `pipenv` may prefer the following command instead:

```bash
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 install command like so:

```bash
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](TESTING.md) 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](PERFORMANCE.md).

Example usage:

1) Compile the network `my_model.tflite`.  The optimised version will be output
to `./output/my_network_vela.tflite`.

```bash
vela my_model.tflite
```

2) Compile the network `/path/to/my_model.tflite` and specify the output to go
in the directory `./results_dir/`.

```bash
vela --output-dir ./results_dir /path/to/my_model.tflite
```

3) Compile a network targeting a particular Ethos-U NPU.  The following command
selects an Ethos-U65 NPU accelerator configured with 512 MAC units.

```bash
vela --accelerator-config ethos-u65-512 my_model.tflite
```
4) Compile a network while minimizing peak SRAM usage, prioritising lower SRAM
usage over runtime performance.

```bash
vela --optimise Size my_model.tflite
```

5) 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.

```bash
vela --optimise Performance my_model.tflite
```

6) 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.

```bash
vela --optimise Performance --arena-cache-size 300000 my_model.tflite
```

7) 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.

```bash
vela --config vela_cfg.ini --system-config My_Sys_Config --memory-mode My_Mem_Mode my_model.tflite
```

8) To get a list of all available options (see CLI Options section below):

```bash
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](API.md).

## Contributions

Please see [Vela Contributions](CONTRIBUTIONS.md).

## Debug Database

Please see [Vela Debug Database](DEBUG_DB.md).

## Options

Please see [Vela CLI Options](OPTIONS.md).  This includes a description of the
system configuration file format.

## Performance

Please see [Vela Performance Estimation Summary](PERFORMANCE.md).

## Releases

Please see [Vela Releases](RELEASES.md).

## Security

Please see [Vela Security](SECURITY.md).

## Supported Operators

Please see [Vela Supported Operators](SUPPORTED_OPS.md) for the list of
operators supported in this release.

## Testing

Please see [Vela Testing](TESTING.md).

## Bug Reporting

Please see [Vela Community Bug Reporting](BUGS.md) for a description of how to
report bugs.

## Resources

Additional useful information:

* [Arm Products: Ethos-U55 NPU](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u55)
* [Arm Products: Ethos-U65 NPU](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u65)
* [Arm Developer: Ethos-U55 NPU](https://developer.arm.com/ip-products/processors/machine-learning/arm-ethos-u/ethos-u55)
* [Arm Developer: Ethos-U65 NPU](https://developer.arm.com/ip-products/processors/machine-learning/arm-ethos-u/ethos-u65)

## License

Vela is licensed under [Apache License 2.0](LICENSE.txt).