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# 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 is tested by comparing the bit exact numerical behaviour of the Ethos-U
optimised operators against that of the corresponding TensorFlow Lite reference
kernels (or TensorFlow Lite for Microcontrollers reference kernels in the case
of the UNIDIRECTIONAL_SEQUENCE_LSTM operator). The following list indicates
which version is used for comparison:
* Vela 4.0.0 to current supports TensorFlow 2.17
* Vela 3.12.0 supports TensorFlow 2.16
* Vela 3.11.0 supports TensorFlow 2.15
* Vela 3.10.0 supports TensorFlow 2.14
* Vela 3.9.0 supports TensorFlow 2.12
* Vela 3.8.0 supports TensorFlow 2.11
* Vela 3.6.0 to 3.7.0 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
## Python Version Support
The majority of Vela's testing is done using a single version of Python, as
indicated by the first version in the list below. However, some additional
testing is also performed across a range of newer versions starting at the
minimum version (pyproject.toml:project.requires-python) indicated in the
brackets:
* Vela 3.10.0 to current supports Python 3.10 (3.9)
* Vela 3.9.0 supports Python 3.10 (3.8)
* Vela 3.8.0 supports Python 3.9 (3.8)
* Vela 3.4.0 to 3.7.0 supports Python 3.7 (3.8)
* Vela 3.3.0 supports Python 3.8 (3.7)
* Vela 0.1.0 to 3.2.0 supports Python 3.6 (3.7)
## Environment
Vela runs on Linux and Microsoft Windows 10 operating systems.
## Prerequisites
The following should be installed prior to the installation of Vela:
* Python 3.10 or compatible
- Development version containing the Python/C API header files
- e.g. `apt install python3.10-dev` or `yum install python310-devel`
* Pip3
* If building from source then C99 and C++17 capable compilers and associated
toolchains are also required
- For Linux operating systems, a GNU toolchain is recommended.
- For Microsoft Windows 10, the Microsoft Visual C++ 14.2 Build Tools are recommended.
See <https://wiki.python.org/moin/WindowsCompilers>
## Installation
Vela is available to install as binary wheels or a source distribution 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 .
```
#### 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 .[dev]
```
If you plan to contribute to the Vela project (highly encouraged!) then it is
recommended to install Vela with the development dependencies (see
[Vela Testing](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/TESTING.md) for more details).
##### Build options for C++ files (ethosu/regor) #####
The C++ part of the the Vela compiler can be configured through the following environment variables:
| Variable | Description |
| :------------------------- | :------------------------------------------------------ |
| CMAKE_BUILD_TYPE | Control cmake-build-type (Release or Debug) |
| CMAKE_BUILD_PARALLEL_LEVEL | Control parallel build level |
| CMAKE_GENERATOR | Override the default CMAKE generator |
| CMAKE_ARGS | Provide additional build-time options (see table below) |
The following build-time options can be provided through CMAKE_ARGS
```bash
# Example (Linux Bash)
CMAKE_ARGS="-DREGOR_ENABLE_LTO=OFF -DREGOR_ENABLE_WERROR=ON" pip3 install -e .[dev]
```
| Option | Description | Arguments |
| :--------------------------------- | :---------------------------------------- | :---------------------------------- |
| REGOR_ENABLE_LTO | Enable Link Time Optimization | ON/OFF |
| REGOR_ENABLE_LDGOLD | Enable Gold linker if available | ON/OFF |
| REGOR_ENABLE_CCACHE | Enable ccache if available | ON/OFF |
| REGOR_ENABLE_WERROR | Enable warnings as errors | ON/OFF |
| REGOR_ENABLE_STD_STATIC | Link libstdc and libgcc statically | ON/OFF |
| REGOR_ENABLE_COVERAGE | Enable Coverage build | ON/OFF |
| REGOR_ENABLE_PROFILING | Enable timer based runtime profiling | ON/OFF |
| REGOR_ENABLE_ASSERT | Enable asserts | ON/OFF |
| REGOR_ENABLE_EXPENSIVE_CHECKS | Enable expensive STL GLICXX asserts | ON/OFF |
| REGOR_ENABLE_RTTI | Enable RTTI (run-time type information) | ON/OFF |
| REGOR_ENABLE_VALGRIND | Enable Valgrind during check target | ON/OFF |
| REGOR_ENABLE_TESTING | Enable unit testing | ON/OFF |
| REGOR_ENABLE_CPPCHECK | Enable CPPCHECK | ON/OFF |
| REGOR_SANITIZE | Sanitizer setting (forwards to fsanitize) | String |
| REGOR_LOG_TRACE_MASK | Log trace enable mask | int (0->7) (See common/logging.hpp) |
| REGOR_PACKAGE_NAME | CPack package name | String |
| REGOR_DEBUG_COMPRESSION | Debug symbol compression | none, zlib, zlib-gnu |
| REGOR_PYTHON_BINDINGS_DESTINATION | Python bindings install destination | String |
| REGOR_PYEXT_VERSION | Python extension version | String |
## 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](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/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.ini`
configuration file located in the config_files directory.
```bash
vela --config Arm/vela.ini --system-config My_Sys_Config --memory-mode My_Mem_Mode my_model.tflite
```
8) To get a list of all available configuration files in the config_files directory:
```bash
vela --list-config-files
```
9) 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>
## Known Issues
### 1. NumPy C API version change
Once ethos-u-vela is installed, the user might want to install a different NumPy
version that is still within the dependency constraints defined in pyproject.toml.
In some scenarios, doing so might prevent ethos-u-vela from functioning as
expected due to incompatibilities between the installed NumPy C headers used in
the mlw_codec and the current version of NumPy.
**Example scenario:**
In the ethos-u-vela source directory, run:
```bash
virtualenv -p 3.10 venv
. venv/bin/activate
pip install ethos-u-vela
```
Next, install a different NumPy version (e.g. 1.21.3)
```bash
pip install numpy==1.21.3 --force
```
Finally, run ethos-u-vela. You might get an error similar to this:
```
ImportError: NumPy C API version mismatch
(Build-time version: 0x10, Run-time version: 0xe)
This is a known issue most likely caused by a change in the API version in
NumPy after installing ethos-u-vela.
```
#### Solution
In order for ethos-u-vela to work with an older version of NumPy that uses
different C APIs, you will need to install the desired NumPy version first, and
then build ethos-u-vela with that specific NumPy version:
1) Uninstall ethos-u-vela and install the desired version of NumPy
```
pip uninstall ethos-u-vela
pip install numpy==1.21.3 --force
```
2) Install required build dependencies
```
pip install "setuptools_scm[toml]<6" wheel
```
3) Install ethos-u-vela without build isolation. Not using build isolation
ensures that the correct version of NumPy is used when copying the C headers
in mlw_codec during the build process.
```
pip install ethos-u-vela --no-build-isolation --no-cache-dir
```
## APIs
Please see [Vela External APIs](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/API.md).
## Bug Reporting
Please see [Vela Community Bug Reporting](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/BUGS.md) for a description of how to
report bugs.
## Contributions
Please see [Vela Contributions](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/CONTRIBUTIONS.md).
## Debug Database
Please see [Vela Debug Database](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/DEBUG_DB.md).
## Inclusive language commitment
This product conforms to Arm’s inclusive language policy and, to the best of
our knowledge, does not contain any non-inclusive language. If you find
something that concerns you, email terms@arm.com.
## Options
Please see [Vela CLI Options](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/OPTIONS.md). This includes a description of the
system configuration file format.
## Performance
Please see [Vela Performance Estimation Summary](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/PERFORMANCE.md).
__NOTE: This is only an estimate. For performance numbers we recommend running
the compiled network on an FVP Model or FPGA.__
## Releases
Please see [Vela Releases](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/RELEASES.md).
## 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 Products: Ethos-U85 NPU](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u85)
* [Arm Developer: Ethos-U55 NPU](https://developer.arm.com/Processors/Ethos-U55)
* [Arm Developer: Ethos-U65 NPU](https://developer.arm.com/Processors/Ethos-U65)
* [Arm Developer: Ethos-U85 NPU](https://developer.arm.com/Processors/Ethos-U85)
## Security
Please see [Vela Security](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/SECURITY.md).
## Supported Operators
Please see [Vela Supported Operators](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/SUPPORTED_OPS.md) for the list of
operators supported in this release.
## Testing
Please see [Vela Testing](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/TESTING.md).
## License
Vela is licensed under [Apache License 2.0](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/LICENSE.txt).
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"description": "<!--\nSPDX-FileCopyrightText: Copyright 2020-2024 Arm Limited and/or its affiliates <open-source-office@arm.com>\n\nSPDX-License-Identifier: Apache-2.0\n\nLicensed under the Apache License, Version 2.0 (the License); you may\nnot use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\nwww.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an AS IS BASIS, WITHOUT\nWARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n-->\n# Vela\n\nThis tool is used to compile a\n[TensorFlow Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers)\nneural network model into an optimised version that can run on an embedded\nsystem containing an\n[Arm Ethos-U NPU](https://www.arm.com/products/silicon-ip-cpu).\n\nIn order to be accelerated by the Ethos-U NPU the network operators must be\nquantised to either 8-bit (unsigned or signed) or 16-bit (signed).\n\nThe optimised model will contain TensorFlow Lite Custom operators for those\nparts of the model that can be accelerated by the Ethos-U NPU. Parts of the\nmodel that cannot be accelerated are left unchanged and will instead run on the\nCortex-M series CPU using an appropriate kernel (such as the\n[Arm](https://www.arm.com) optimised\n[CMSIS-NN](https://github.com/ARM-software/CMSIS_5/tree/develop/CMSIS/NN)\nkernels).\n\nAfter compilation the optimised model can only be run on an Ethos-U NPU\nembedded system.\n\nThe tool will also generate performance estimates (EXPERIMENTAL) for the\ncompiled model.\n\nThe tool has limited functionality for compiling a\n[TOSA](https://git.mlplatform.org/tosa/specification.git/) neural network\n(EXPERIMENTAL).\n\n## TensorFlow Support\n\nVela is tested by comparing the bit exact numerical behaviour of the Ethos-U\noptimised operators against that of the corresponding TensorFlow Lite reference\nkernels (or TensorFlow Lite for Microcontrollers reference kernels in the case\nof the UNIDIRECTIONAL_SEQUENCE_LSTM operator). The following list indicates\nwhich version is used for comparison:\n\n* Vela 4.0.0 to current supports TensorFlow 2.17\n* Vela 3.12.0 supports TensorFlow 2.16\n* Vela 3.11.0 supports TensorFlow 2.15\n* Vela 3.10.0 supports TensorFlow 2.14\n* Vela 3.9.0 supports TensorFlow 2.12\n* Vela 3.8.0 supports TensorFlow 2.11\n* Vela 3.6.0 to 3.7.0 supports TensorFlow 2.10\n* Vela 3.5.0 supports TensorFlow 2.9\n* Vela 3.4.0 supports TensorFlow 2.8\n* Vela 3.3.0 supports TensorFlow 2.7\n* Vela 3.1.0 to 3.2.0 supports TensorFlow 2.5\n* Vela 2.1.0 to 3.0.0 supports TensorFlow 2.4\n* Vela 2.0.0 to 2.0.1 supports TensorFlow 2.3\n* Vela 0.1.0 to 1.2.0 supports TensorFlow 2.1\n\n## Python Version Support\n\nThe majority of Vela's testing is done using a single version of Python, as\nindicated by the first version in the list below. However, some additional\ntesting is also performed across a range of newer versions starting at the\nminimum version (pyproject.toml:project.requires-python) indicated in the\nbrackets:\n\n* Vela 3.10.0 to current supports Python 3.10 (3.9)\n* Vela 3.9.0 supports Python 3.10 (3.8)\n* Vela 3.8.0 supports Python 3.9 (3.8)\n* Vela 3.4.0 to 3.7.0 supports Python 3.7 (3.8)\n* Vela 3.3.0 supports Python 3.8 (3.7)\n* Vela 0.1.0 to 3.2.0 supports Python 3.6 (3.7)\n\n## Environment\n\nVela runs on Linux and Microsoft Windows 10 operating systems.\n\n## Prerequisites\n\nThe following should be installed prior to the installation of Vela:\n\n* Python 3.10 or compatible\n - Development version containing the Python/C API header files\n - e.g. `apt install python3.10-dev` or `yum install python310-devel`\n* Pip3\n* If building from source then C99 and C++17 capable compilers and associated\ntoolchains are also required\n - For Linux operating systems, a GNU toolchain is recommended.\n - For Microsoft Windows 10, the Microsoft Visual C++ 14.2 Build Tools are recommended.\n See <https://wiki.python.org/moin/WindowsCompilers>\n\n## Installation\n\nVela is available to install as binary wheels or a source distribution from\n[PyPi](https://pypi.org/project/ethos-u-vela/), or as\nsource code from\n[ML Platform](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela).\nBoth methods will automatically install all the required dependencies.\n\n### PyPi\n\nInstall Vela from PyPi using the following command:\n\n```bash\npip3 install ethos-u-vela\n```\n\n### ML Platform\n\nFirst obtain the source code by either downloading the desired TGZ file from: \n<https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela>\n\nOr by cloning the git repository:\n\n```bash\ngit clone https://review.mlplatform.org/ml/ethos-u/ethos-u-vela.git\n```\n\nOnce you have the source code, Vela can be installed using the following\ncommand from the root directory of the repository:\n\n```bash\npip3 install .\n```\n\n#### Advanced Installation for Developers\n\nIf you plan to modify the Vela codebase then it is recommended to install Vela\nas an editable package to avoid the need to re-install after every modification.\nThis is done by adding the `-e` option to the install command like so:\n\n```bash\npip3 install -e .[dev]\n```\n\nIf you plan to contribute to the Vela project (highly encouraged!) then it is\nrecommended to install Vela with the development dependencies (see\n[Vela Testing](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/TESTING.md) for more details).\n\n##### Build options for C++ files (ethosu/regor) #####\n\nThe C++ part of the the Vela compiler can be configured through the following environment variables:\n\n| Variable | Description |\n| :------------------------- | :------------------------------------------------------ |\n| CMAKE_BUILD_TYPE | Control cmake-build-type (Release or Debug) |\n| CMAKE_BUILD_PARALLEL_LEVEL | Control parallel build level |\n| CMAKE_GENERATOR | Override the default CMAKE generator |\n| CMAKE_ARGS | Provide additional build-time options (see table below) |\n\nThe following build-time options can be provided through CMAKE_ARGS \n```bash\n# Example (Linux Bash)\nCMAKE_ARGS=\"-DREGOR_ENABLE_LTO=OFF -DREGOR_ENABLE_WERROR=ON\" pip3 install -e .[dev]\n```\n| Option | Description | Arguments |\n| :--------------------------------- | :---------------------------------------- | :---------------------------------- |\n| REGOR_ENABLE_LTO | Enable Link Time Optimization | ON/OFF |\n| REGOR_ENABLE_LDGOLD | Enable Gold linker if available | ON/OFF |\n| REGOR_ENABLE_CCACHE | Enable ccache if available | ON/OFF |\n| REGOR_ENABLE_WERROR | Enable warnings as errors | ON/OFF |\n| REGOR_ENABLE_STD_STATIC | Link libstdc and libgcc statically | ON/OFF |\n| REGOR_ENABLE_COVERAGE | Enable Coverage build | ON/OFF |\n| REGOR_ENABLE_PROFILING | Enable timer based runtime profiling | ON/OFF |\n| REGOR_ENABLE_ASSERT | Enable asserts | ON/OFF |\n| REGOR_ENABLE_EXPENSIVE_CHECKS | Enable expensive STL GLICXX asserts | ON/OFF |\n| REGOR_ENABLE_RTTI | Enable RTTI (run-time type information) | ON/OFF |\n| REGOR_ENABLE_VALGRIND | Enable Valgrind during check target | ON/OFF |\n| REGOR_ENABLE_TESTING | Enable unit testing | ON/OFF |\n| REGOR_ENABLE_CPPCHECK | Enable CPPCHECK | ON/OFF |\n| REGOR_SANITIZE | Sanitizer setting (forwards to fsanitize) | String |\n| REGOR_LOG_TRACE_MASK | Log trace enable mask | int (0->7) (See common/logging.hpp) |\n| REGOR_PACKAGE_NAME | CPack package name | String |\n| REGOR_DEBUG_COMPRESSION | Debug symbol compression | none, zlib, zlib-gnu |\n| REGOR_PYTHON_BINDINGS_DESTINATION | Python bindings install destination | String |\n| REGOR_PYEXT_VERSION | Python extension version | String |\n\n## Running\n\nVela is run with an input `.tflite` or `.tosa` (EXPERIMENTAL) file passed on the\ncommand line. This file contains the neural network to be compiled. The tool then\noutputs an optimised `.tflite` file with a `_vela` suffix in the file name, along\nwith performance estimate (EXPERIMENTAL) CSV files, all to the output directory.\nIt also prints a performance estimation summary back to the console, see\n[Vela Performance Estimation Summary](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/PERFORMANCE.md).\n\nExample usage:\n\n1) Compile the network `my_model.tflite`. The optimised version will be output\nto `./output/my_network_vela.tflite`.\n\n```bash\nvela my_model.tflite\n```\n\n2) Compile the network `/path/to/my_model.tflite` and specify the output to go\nin the directory `./results_dir/`.\n\n```bash\nvela --output-dir ./results_dir /path/to/my_model.tflite\n```\n\n3) Compile a network targeting a particular Ethos-U NPU. The following command\nselects an Ethos-U65 NPU accelerator configured with 512 MAC units.\n\n```bash\nvela --accelerator-config ethos-u65-512 my_model.tflite\n```\n4) Compile a network while minimizing peak SRAM usage, prioritising lower SRAM\nusage over runtime performance.\n\n```bash\nvela --optimise Size my_model.tflite\n```\n\n5) Compile a network to have maximum performance, i.e. the fastest inference time.\nThis prioritises a higher runtime performance over a lower peak SRAM usage.\n\n```bash\nvela --optimise Performance my_model.tflite\n```\n\n6) Compile a network while optimising for the fastest inference time possible,\nwith an upper bound for the SRAM usage. The memory limit is set in bytes, i.e.\nrun the following example if one requires a limit of 300KB.\n\n```bash\nvela --optimise Performance --arena-cache-size 300000 my_model.tflite\n```\n\n7) Compile a network using a particular embedded system configuration defined in\nVela's configuration file. The following command selects the `My_Sys_Config`\nsystem configuration along with the `My_Mem_Mode` memory mode from the `vela.ini`\nconfiguration file located in the config_files directory.\n\n```bash\nvela --config Arm/vela.ini --system-config My_Sys_Config --memory-mode My_Mem_Mode my_model.tflite\n```\n\n8) To get a list of all available configuration files in the config_files directory:\n\n```bash\nvela --list-config-files\n```\n\n9) To get a list of all available options (see CLI Options section below):\n\n```bash\nvela --help\n```\n\n## Warnings\n\nWhen running the Vela compiler it may report a number of warning messages to the\nconsole. These should all be thoroughly reviewed as they will indicate decisions\nthat the compiler has made in order to create the optimised network.\n\n## Example Networks\n\nSome example networks that contain quantised operators which can be compiled by\nVela to run on the Ethos-U NPU can be found at:\n<https://tfhub.dev/s?deployment-format=lite&q=quantized>\n\n## Known Issues\n\n### 1. NumPy C API version change\n\nOnce ethos-u-vela is installed, the user might want to install a different NumPy\nversion that is still within the dependency constraints defined in pyproject.toml.\n\nIn some scenarios, doing so might prevent ethos-u-vela from functioning as\nexpected due to incompatibilities between the installed NumPy C headers used in\nthe mlw_codec and the current version of NumPy.\n\n**Example scenario:**\n\nIn the ethos-u-vela source directory, run:\n\n```bash\nvirtualenv -p 3.10 venv\n. venv/bin/activate\npip install ethos-u-vela\n```\n\nNext, install a different NumPy version (e.g. 1.21.3)\n\n```bash\npip install numpy==1.21.3 --force\n```\n\nFinally, run ethos-u-vela. You might get an error similar to this:\n\n```\nImportError: NumPy C API version mismatch\n(Build-time version: 0x10, Run-time version: 0xe)\nThis is a known issue most likely caused by a change in the API version in\nNumPy after installing ethos-u-vela.\n```\n\n#### Solution\n\nIn order for ethos-u-vela to work with an older version of NumPy that uses\ndifferent C APIs, you will need to install the desired NumPy version first, and\nthen build ethos-u-vela with that specific NumPy version:\n\n1) Uninstall ethos-u-vela and install the desired version of NumPy\n ```\n pip uninstall ethos-u-vela\n pip install numpy==1.21.3 --force\n ```\n\n2) Install required build dependencies\n ```\n pip install \"setuptools_scm[toml]<6\" wheel\n ```\n\n3) Install ethos-u-vela without build isolation. Not using build isolation\n ensures that the correct version of NumPy is used when copying the C headers\n in mlw_codec during the build process.\n ```\n pip install ethos-u-vela --no-build-isolation --no-cache-dir\n ```\n\n## APIs\n\nPlease see [Vela External APIs](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/API.md).\n\n## Bug Reporting\n\nPlease see [Vela Community Bug Reporting](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/BUGS.md) for a description of how to\nreport bugs.\n\n## Contributions\n\nPlease see [Vela Contributions](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/CONTRIBUTIONS.md).\n\n## Debug Database\n\nPlease see [Vela Debug Database](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/DEBUG_DB.md).\n\n## Inclusive language commitment\n\nThis product conforms to Arm\u2019s inclusive language policy and, to the best of\nour knowledge, does not contain any non-inclusive language. If you find\nsomething that concerns you, email terms@arm.com.\n\n## Options\n\nPlease see [Vela CLI Options](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/OPTIONS.md). This includes a description of the\nsystem configuration file format.\n\n## Performance\n\nPlease see [Vela Performance Estimation Summary](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/PERFORMANCE.md).\n__NOTE: This is only an estimate. For performance numbers we recommend running\nthe compiled network on an FVP Model or FPGA.__\n\n## Releases\n\nPlease see [Vela Releases](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/RELEASES.md).\n\n## Resources\n\nAdditional useful information:\n\n* [Arm Products: Ethos-U55 NPU](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u55)\n* [Arm Products: Ethos-U65 NPU](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u65)\n* [Arm Products: Ethos-U85 NPU](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u85)\n* [Arm Developer: Ethos-U55 NPU](https://developer.arm.com/Processors/Ethos-U55)\n* [Arm Developer: Ethos-U65 NPU](https://developer.arm.com/Processors/Ethos-U65)\n* [Arm Developer: Ethos-U85 NPU](https://developer.arm.com/Processors/Ethos-U85)\n\n## Security\n\nPlease see [Vela Security](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/SECURITY.md).\n\n## Supported Operators\n\nPlease see [Vela Supported Operators](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/SUPPORTED_OPS.md) for the list of\noperators supported in this release.\n\n## Testing\n\nPlease see [Vela Testing](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/TESTING.md).\n\n## License\n\nVela is licensed under [Apache License 2.0](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela/+/refs/tags/4.1.0/LICENSE.txt).\n",
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