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Machine Learning inference engine for Microcontrollers and Embedded devices
Python C C++
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README.md

Travis CI Build Status Appveyor Build status DOI

emlearn

Machine learning for microcontroller and embedded systems. Train in Python, then do inference on any device with a C99 compiler.

Key features

Embedded-friendly Inference

  • Portable C99 code
  • No libc required
  • No dynamic allocations
  • Support integer/fixed-point math
  • Single header file include

Convenient Training

  • Using Python with scikit-learn or Keras
  • The generated C classifier is also accessible in Python

MIT licensed

Can be used as an open source alternative to MATLAB Classification Trees, Decision Trees using MATLAB Coder for C/C++ code generation. fitctree, fitcensemble, TreeBagger, ClassificationEnsemble, CompactTreeBagger

Status

Minimally useful

Classifiers:

  • eml_trees: sklearn.RandomForestClassifier, sklearn.ExtraTreesClassifier, sklearn.DecisionTreeClassifier
  • eml_net: sklearn.MultiLayerPerceptron, Keras.Sequential with fully-connected layers
  • eml_bayes: sklearn.GaussianNaiveBayes

Feature extraction:

  • eml_audio: Melspectrogram

Tested running on AVR Atmega, ESP8266 and Linux.

Installing

Install from PyPI

pip install --user emlearn

Usage

  1. Train your model in Python
from sklearn.ensemble import RandomForestClassifier
estimator = RandomForestClassifier(n_estimators=10, max_depth=10)
estimator.fit(X_train, Y_train)
...
  1. Convert it to C code
import emlearn
cmodel = emlearn.convert(estimator, method='inline')
cmodel.save(file='sonar.h')
  1. Use the C code
#include "sonar.h"

const int32_t length = 60;
int32_t values[length] = { ... };
const int32_t predicted_class = sonar_predict(values, length):

For full example code, see examples/digits.py

Citations

If you use emlearn in an academic work, please reference it using:

@misc{emlearn,
  author       = {Jon Nordby},
  title        = {{emlearn: Machine Learning inference engine for 
                   Microcontrollers and Embedded Devices}},
  month        = mar,
  year         = 2019,
  doi          = {10.5281/zenodo.2589394},
  url          = {https://doi.org/10.5281/zenodo.2589394}
}
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