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Logo

EdgeVolution is a software-hardware end-to-end pipeline that allows to optimize preprocessing and neural network architectures for microcontrollers in an end-to-end fashion. It features different building blocks that can be customized individually.

Overview

Getting Started

Welcome to EdgeVolution! You can follow this simple guide here to get a high-level overview. You can find a more comprehensive documentation here.

Cloning the repository

git clone https://github.com/ankilab/EdgeVolution

Building the Docker Image

EdgeVolution provides two Docker build targets:

ML/NAS target (default) — For running neural architecture search and training:

docker build -t edgevolution .

Embedded target — For flashing optimized models to MCU hardware (includes nRF tools, J-Link, Zephyr SDK):

docker build --target embedded -t edgevolution-embedded .

Note: The embedded target downloads the Zephyr SDK and modules (~1.5 GB) during the build. This is cached for subsequent builds.

Running the Container

ML/NAS (GPU-accelerated):

docker run -it --rm --gpus all -v $(pwd):/EdgeVolution edgevolution

Embedded (with USB device passthrough for J-Link):

docker run -it --rm --privileged --gpus all -v $(pwd):/EdgeVolution edgevolution-embedded

Running Tests

Inside the container:

python3 -m pytest tests/ -v -p no:dash

Defining hyperparameters

Before the EdgeVolution optimization run is started, some configurations must be made. These include the definition of hyperparameters, the search space and the boards that are to be used for evaluating the candidates on the microcontroller.

  1. Search space setup search space setup

  2. Hyperparameters hyperparameters --> Important to update results path!

  3. Microcontroller setup microcontroller boards

Running an experiment

python main.py +hyperparameters=speech_commands +search_space=speech_commands +boards=none

Search Strategies

EdgeVolution supports multiple search strategies via a pluggable ask/tell interface. Select a strategy with the search_strategy= flag:

Strategy Flag Description
Genetic Algorithm search_strategy=genetic_algorithm Standard GA with crossover and mutation (default)
Random Search search_strategy=random Random baseline — no selection or crossover
Regularized Evolution search_strategy=regularized_evolution Tournament selection with aging (Real et al. 2019)
PyMOO (NSGA-II) search_strategy=pymoo Multi-objective optimization (requires pymoo)
Ax (Bayesian Opt.) search_strategy=ax Bayesian optimization via BoTorch (requires ax-platform)

Example:

python main.py +hyperparameters=speech_commands +search_space=speech_commands +boards=none search_strategy=regularized_evolution

For more examples and common commands, see the Usage Guide.

Community Support

Community support is provided via a Discord Server for real-time community support.

Contributing to EdgeVolution

To contribute to EdgeVolution, follow these steps:

  1. Fork this repository.
  2. Create a branch: git checkout -b <branch_name>.
  3. Make your changes and commit them: git commit -m '<commit_message>'
  4. Push to the original branch: git push origin <project_name>/<location>
  5. Create the pull request.

Alternatively see the GitHub documentation on creating a pull request.

Contact

If you want to contact me you can create a github issue or reach out on Discord.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Citation

tbd