Qualia toolchain Core
Project description
Qualia Core (formerly MicroAI)
End-to-end training, quantization and deployment framework for deep neural networks on microcontrollers.
Repository should be cloned with --recursive
to get TFLite Micro and its dependencies.
Dependencies
Python:
numpy
scikit-learn
tomlkit
colorful
gitpython
Dataset
GTSRB
Python:
imageio
scikit-image
Training
TensorFlow
Python:
tensorflow
tensorflow_addons
PyTorch
Python:
pytorch
pytorch_lightning
Deployment
Embedded targets
SparkFun Edge
Python:
pycryptodome
Nucleo-L452RE-P
System:
stm32cubeide
stm32cubeprog
Embedded frameworks
STM32Cube.AI
STM32CubeIDE extension pack:
X-CUBE-AI == 5.2.0
TensorFlow Lite Micro
System:
arm-none-eabi-binutils
arm-none-eabi-gcc
arm-none-eabi-newlib
libopenexr-dev
wget
Qualia-CodeGen
Python:
jinja2
System:
arm-none-eabi-binutils
arm-none-eabi-gcc
arm-none-eabi-newlib
Evaluation
Python:
pyserial
Usage
If Qualia installed with pip, you can run the qualia
command directly. Otherwise run PYTHONPATH=. ./bin/qualia <config.toml> <action>
from the qualia directory.
Dataset pre-processing
qualia <config.toml> preprocess_data
Training
qualia <config.toml> train
Prepare deployment (generate firmware)
qualia <config.toml> prepare_deploy
Deploy and evaluate
qualia <config.toml> deploy_and_evaluate
Run test suite
CUBLAS_WORKSPACE_CONFIG=:4096:8 PYTHONHASHSEED=2 python -m unittest discover qualia/tests
Included support for datasets, learning framework, neural networks, embedded frameworks and targets
Datasets
Learning frameworks
- TensorFlow.Keras
- PyTorch
Neural networks
- MLP
- CNN (1D&2D)
- Resnetv1 (1D&2D)
Embedded frameworks
- STM32Cube.AI
- TensorFlow Lite for Microcontrollers
- Qualia-CodeGen
Targets
- Nucleo-L452RE-P
- SparkFun Edge
Reference & Citation
Quantization and Deployment of Deep Neural Networks on Microcontrollers, Pierre-Emmanuel Novac, Ghouthi Boukli Hacene, Alain Pegatoquet, Benoît Miramond and Vincent Gripon, Sensors, 2021.
@article{qualia,
author = {Novac, Pierre-Emmanuel and Boukli Hacene, Ghouthi and Pegatoquet, Alain and Miramond, Benoît and Gripon, Vincent},
title = {Quantization and Deployment of Deep Neural Networks on Microcontrollers},
journal = {Sensors},
volume = {21},
year = {2021},
number = {9},
article-number = {2984},
url = {https://www.mdpi.com/1424-8220/21/9/2984},
issn = {1424-8220},
doi = {10.3390/s21092984}
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for qualia_core-2.2.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a0c061c8fffda001df42631dda7d9b6c48dde0ddc23bc54033307073c2995d2f |
|
MD5 | 9cc58c8ae501a87edf74dd3de8fac4ed |
|
BLAKE2b-256 | 97cb800096c9157841dfaba48528641403b6935dccf74a87aaeb09352e62cc85 |