# 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 ## 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: 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: ## 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).