| Name | ethos-u-vela JSON |
| Version |
4.4.1
JSON |
| download |
| home_page | None |
| Summary | Neural network model compiler for Arm Ethos-U NPUs |
| upload_time | 2025-10-08 11:49:15 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | ~=3.9 |
| license | None |
| keywords |
ethos-u
vela
tosa
tflite
npu
compiler
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
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Licensed under the Apache License, Version 2.0 (the License); you may
not use this file except in compliance with the License.
You may obtain a copy of the License at
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# Vela
Vela is a compiler for [Arm Ethos-U NPU](https://www.arm.com/products/silicon-ip-cpu)
edge AI devices. It is used to convert neural networks defined in [TOSA](https://www.mlplatform.org/tosa/)
or [TensorFlow Lite/LiteRT](https://ai.google.dev/edge/litert) format into
Ethos-U command streams.
The input network must have quantised activations and quantised weights in order
to be accelerated by the Ethos-U.
More details about Ethos-U devices:
* Ethos-U55, [product](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u55) and [developer](https://developer.arm.com/Processors/Ethos-U55) information
* Ethos-U65, [product](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u65) and [developer](https://developer.arm.com/Processors/Ethos-U65) information
* Ethos-U85, [product](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u85) and [developer](https://developer.arm.com/Processors/Ethos-U85) information
## Installation
Vela runs on:
* Apple macOS
* Linux
* Microsoft Windows
The simplest way to obtain Vela is to install it directly from [PyPi](https://pypi.org/project/ethos-u-vela/).
The following will install the latest released version:
```bash
pip3 install ethos-u-vela
```
### Requirements
If your system does not match one of the pre-built binary wheels on PyPi then
the installation process will attempt to build Vela from its source code. To do
this it requires the following:
* Development version of Python 3 containing the Python/C API header files
- e.g. `apt install python3.10-dev`
* C99 and C++17 capable compiler and toolchain
- For Apple macOS; XCode or Command Line Tools are recommended,
see <https://developer.apple.com/xcode/resources/> or `xcode-select --install`
- For Linux operating systems; a GNU or Clang toolchain is recommended,
e.g. `apt install build-essential`
- For Microsoft Windows 10; the Microsoft Visual C++ 14.2 Build Tools are recommended,
see <https://wiki.python.org/moin/WindowsCompilers>
* CMake
- See <https://cmake.org/download/>
### Manually Building
The source code is available from [Arm's GitLab Instance](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela),
see [Building From Source](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/BUILD.md) for more details.
## Getting Started
Once installed, Vela can be run from the command line by specifying just two
settings:
* Target Ethos-U device; `--accelerator-config`
* Neural network file; `.tflite` or `.tosa`
```bash
vela --accelerator-config ethos-u55-128 my_network.tflite
```
However, it is highly recommended that you specify some additional settings that
describe the Ethos-U system and what optimisation strategy the compiler should
use. The three additional settings are:
* System configuration; `--system-config`
* Memory mode; `--memory-mode`
* Optimisation strategy; `--optimise`
The system configuration and memory mode refer to definitions in a configuration
file. These are specific to the target system. However an example
configuration (`Arm/vela.ini`) containing some generic reference systems is
provided as part of the installation.
The optimisation strategy indicates whether the compiler should minimise
inference time or runtime memory usage.
See [CLI Options](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/OPTIONS.md) for more information.
Example of how to set the system configuration and memory mode:
```bash
vela --config Arm/vela.ini --system-config Ethos_U85_SYS_DRAM_High --memory-mode Dedicated_Sram_384KB --accelerator-config ethos-u85-256 my_network.tosa
```
Example of how to set the optimisation strategy:
```bash
vela --optimise Size --accelerator-config ethos-u55-64 my_network.tflite
```
Command to list all known configuration files:
```bash
vela --list-config-files
```
Command to list all configurations in a configuration file:
```bash
vela --list-configs Arm/vela.ini
```
### Output
The result of the compilation is an optimised network in either TFLite or Raw
format depending upon the input network. This can be overridden using the
`--output-format option`.
TFLite output contains TensorFlow Lite Custom operators for those parts of
the network that can be accelerated by the Ethos-U NPU. Parts of the network
that cannot be accelerated are left unchanged.
Raw output contains the command stream and weight data required to run Ethos-U
parts of the optimised network. This is stored in [`.npz`](https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html#module-numpy.lib.format)
format. See the Output format [CLI Option](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/OPTIONS.md) for more information.
This does not contain CPU parts of the network and so cannot be used for
converting TFLite to TOSA.
#### Warnings
It is important to thoroughly review and understand all warning messages
generated by the compiler as they could indicate that not all operators were
optimally converted to run on the Ethos-U.
#### Performance information
A performance estimation summary is reported after compilation. The cycle and
bandwidth numbers should not be taken as accurate representations of real
performance numbers and they should not be compared against those from other
compilations that use different settings or configurations. The numbers
reported allow the compiler to make its optimisation decisions only. For
accurate performance numbers the network should be run and profiled on an FPGA.
For approximate performance numbers the network can be run on a Fixed
Virtual Platform (FVP) Model.
## Additional Vela Information
* [External APIs](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/API.md)
* [Bug Reporting](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/BUGS.md)
* [Building From Source](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/BUILD.md)
* [Contributing](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/CONTRIBUTING.md)
* [CLI Options](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/OPTIONS.md)
* [Debug Database](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/DEBUG_DB.md)
* [Performance Estimations](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/PERFORMANCE.md)
* [Operator Support](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/SUPPORTED_OPS.md)
* [Changelog](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/CHANGELOG.md)
* [Security Vulnerabilities](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/SECURITY.md)
* [Testing](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/TESTING.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 then email [terms@arm.com](mailto:terms@arm.com).
## License
Vela is licensed under [Apache License 2.0](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/LICENSE.txt).
Raw data
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"description": "<!--\nSPDX-FileCopyrightText: Copyright 2020-2025 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\nVela is a compiler for [Arm Ethos-U NPU](https://www.arm.com/products/silicon-ip-cpu)\nedge AI devices. It is used to convert neural networks defined in [TOSA](https://www.mlplatform.org/tosa/)\nor [TensorFlow Lite/LiteRT](https://ai.google.dev/edge/litert) format into\nEthos-U command streams.\n\nThe input network must have quantised activations and quantised weights in order\nto be accelerated by the Ethos-U.\n\nMore details about Ethos-U devices:\n* Ethos-U55, [product](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u55) and [developer](https://developer.arm.com/Processors/Ethos-U55) information\n* Ethos-U65, [product](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u65) and [developer](https://developer.arm.com/Processors/Ethos-U65) information\n* Ethos-U85, [product](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u85) and [developer](https://developer.arm.com/Processors/Ethos-U85) information\n\n## Installation\n\nVela runs on:\n* Apple macOS\n* Linux\n* Microsoft Windows\n\nThe simplest way to obtain Vela is to install it directly from [PyPi](https://pypi.org/project/ethos-u-vela/).\nThe following will install the latest released version:\n\n```bash\npip3 install ethos-u-vela\n```\n\n### Requirements\n\nIf your system does not match one of the pre-built binary wheels on PyPi then\nthe installation process will attempt to build Vela from its source code. To do\nthis it requires the following:\n* Development version of Python 3 containing the Python/C API header files\n - e.g. `apt install python3.10-dev`\n* C99 and C++17 capable compiler and toolchain\n - For Apple macOS; XCode or Command Line Tools are recommended,\n see <https://developer.apple.com/xcode/resources/> or `xcode-select --install`\n - For Linux operating systems; a GNU or Clang toolchain is recommended,\n e.g. `apt install build-essential`\n - For Microsoft Windows 10; the Microsoft Visual C++ 14.2 Build Tools are recommended,\n see <https://wiki.python.org/moin/WindowsCompilers>\n* CMake\n - See <https://cmake.org/download/>\n\n### Manually Building\n\nThe source code is available from [Arm's GitLab Instance](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela),\nsee [Building From Source](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/BUILD.md) for more details.\n\n## Getting Started\n\nOnce installed, Vela can be run from the command line by specifying just two\nsettings:\n* Target Ethos-U device; `--accelerator-config`\n* Neural network file; `.tflite` or `.tosa`\n\n```bash\nvela --accelerator-config ethos-u55-128 my_network.tflite\n```\n\nHowever, it is highly recommended that you specify some additional settings that\ndescribe the Ethos-U system and what optimisation strategy the compiler should\nuse. The three additional settings are:\n* System configuration; `--system-config`\n* Memory mode; `--memory-mode`\n* Optimisation strategy; `--optimise`\n\nThe system configuration and memory mode refer to definitions in a configuration\nfile. These are specific to the target system. However an example\nconfiguration (`Arm/vela.ini`) containing some generic reference systems is\nprovided as part of the installation.\n\nThe optimisation strategy indicates whether the compiler should minimise\ninference time or runtime memory usage.\n\nSee [CLI Options](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/OPTIONS.md) for more information.\n\nExample of how to set the system configuration and memory mode:\n```bash\nvela --config Arm/vela.ini --system-config Ethos_U85_SYS_DRAM_High --memory-mode Dedicated_Sram_384KB --accelerator-config ethos-u85-256 my_network.tosa\n```\n\nExample of how to set the optimisation strategy:\n```bash\nvela --optimise Size --accelerator-config ethos-u55-64 my_network.tflite\n```\n\nCommand to list all known configuration files:\n```bash\nvela --list-config-files\n```\n\nCommand to list all configurations in a configuration file:\n```bash\nvela --list-configs Arm/vela.ini\n```\n\n### Output\n\nThe result of the compilation is an optimised network in either TFLite or Raw\nformat depending upon the input network. This can be overridden using the\n`--output-format option`.\n\nTFLite output contains TensorFlow Lite Custom operators for those parts of\nthe network that can be accelerated by the Ethos-U NPU. Parts of the network\nthat cannot be accelerated are left unchanged.\n\nRaw output contains the command stream and weight data required to run Ethos-U\nparts of the optimised network. This is stored in [`.npz`](https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html#module-numpy.lib.format)\nformat. See the Output format [CLI Option](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/OPTIONS.md) for more information.\nThis does not contain CPU parts of the network and so cannot be used for\nconverting TFLite to TOSA.\n\n#### Warnings\n\nIt is important to thoroughly review and understand all warning messages\ngenerated by the compiler as they could indicate that not all operators were\noptimally converted to run on the Ethos-U.\n\n#### Performance information\n\nA performance estimation summary is reported after compilation. The cycle and\nbandwidth numbers should not be taken as accurate representations of real\nperformance numbers and they should not be compared against those from other\ncompilations that use different settings or configurations. The numbers\nreported allow the compiler to make its optimisation decisions only. For\naccurate performance numbers the network should be run and profiled on an FPGA.\nFor approximate performance numbers the network can be run on a Fixed\nVirtual Platform (FVP) Model.\n\n## Additional Vela Information\n* [External APIs](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/API.md)\n* [Bug Reporting](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/BUGS.md)\n* [Building From Source](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/BUILD.md)\n* [Contributing](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/CONTRIBUTING.md)\n* [CLI Options](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/OPTIONS.md)\n* [Debug Database](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/DEBUG_DB.md)\n* [Performance Estimations](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/PERFORMANCE.md)\n* [Operator Support](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/SUPPORTED_OPS.md)\n* [Changelog](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/CHANGELOG.md)\n* [Security Vulnerabilities](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/SECURITY.md)\n* [Testing](https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela/-/blob/4.4.1/TESTING.md)\n\n## Inclusive language commitment\n\nThis product conforms to Arm\u2019s inclusive language policy and, to the best of our\nknowledge, does not contain any non-inclusive language. 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