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https://github.com/emlearn/emlearn-micropython

Efficient Machine Learning engine for MicroPython
https://github.com/emlearn/emlearn-micropython

embedded-systems machine-learning micropython python tinyml

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Efficient Machine Learning engine for MicroPython

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README 

        
[![DOI](https://zenodo.org/badge/670384512.svg)](https://zenodo.org/badge/latestdoi/670384512)



# emlearn-micropython



[Micropython](https://micropython.org) integration for the [emlearn](https://emlearn.org) Machine Learning library for microcontrollers.



It enables MicroPython applications to run efficient Machine Learning models on microcontroller,

without having to touch any C code.



> scikit-learn for Microcontrollers



This is a [TinyML](https://www.tinyml.org/) library,

particularly well suited for low-compexity and low-power classification tasks.

It can be combined with feature preprocessing, including neural networks to address more complex tasks.



## Status

**Minimally useful**



- Has been tested on `armv6m` (RP2040) and `x64` (Unix port) 

- Pre-built modules are available for the most common architectures/devices



## Features



- Classification with [RandomForest](https://en.wikipedia.org/wiki/Random_forest)/DecisionTree models

- Classification and on-device learning with [K-Nearest Neighbors (KNN)](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)

- Installable as a MicroPython native module. No rebuild/flashing needed

- Models can be loaded at runtime from a .CSV file in disk/flash

- Highly efficient. Inference times down to 100 microseconds, RAM usage <2 kB, FLASH usage <2 kB



## Prerequisites



Minimally you will need



- Python 3.10+ on host

- MicroPython 1.20+ running onto your device



#### Download repository



Download the repository with examples etc

```

git clone https://github.com/emlearn/emlearn-micropython

```



## Installing from a release



#### Find architecture



Identify which CPU architecture your device uses.

You need to specify `ARCH` to install the correct module version.



| ARCH          | Description                       | Examples              |

|---------------|-----------------------------------|---------------------- |

| x64           | x86 64 bit                        | PC                    |

| x86           | x86 32 bit                        |                       |

| armv6m        | ARM Thumb (1)                     | Cortex-M0             |

| armv7m        | ARM Thumb 2                       | Cortex-M3             |

| armv7emsp     | ARM Thumb 2, single float         | Cortex-M4F, Cortex-M7 |

| armv7emdp     | ARM Thumb 2, double floats        | Cortex-M7             |

| xtensa        | non-windowed                      | ESP8266               |

| xtensawin     | windowed with window size 8       | ESP32                 |



#### Download release files



Download from [releases](https://github.com/emlearn/emlearn-micropython/releases).



#### Install on device



Copy the .mpy file for the correct `ARCH` to your device.

```

mpremote cp emltrees.mpy :emltrees.mpy

mpremote cp emlneighbors.mpy :emlneighbors.mpy

```



NOTE: If there is no ready-made build for your device/architecture,

then you will need to build the .mpy module yourself.



## Usage



NOTE: Make sure to install the module first (see above)



Train a model with scikit-learn

```

pip install emlearn scikit-learn

python examples/xor_train.py

```



Copy model file to device



```

mpremote cp xor_model.csv :xor_model.csv

```



Run program that uses the model



```

mpremote run examples/xor_run.py

```



## Benchmarks



#### UCI handwriting digits



UCI ML hand-written digits datasets dataset from

[sklearn.datasets.load_digits](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html).

8x8 image, 64 features. Values are 4-bit integers (16 levels). 10 classes.



Running with a very simple RandomForest, 7 trees.

Reaches approx 86% accuracy.

Tested on Raspberry PI Pico, with RP2040 microcontroller (ARM Cortex M0 @ 133 MHz).



![Inferences per second](./benchmarks/digits_bench.png)



NOTE: over half of the time for emlearn case,

is spent on converting the Python lists of integers into a float array.

Removing that bottleneck would speed up things considerably.



## Developing locally



#### Prerequisites

These come in addition to the prequisites described above.



Make sure you have the dependencies needed to build for your platform.

See [MicroPython: Building native modules](https://docs.micropython.org/en/latest/develop/natmod.html).



We assume that micropython is installed in the same place as this repository.

If using another location, adjust `MPY_DIR` accordingly.



NOTE: As of August 2023, an out-of-tree patch is needed for MicroPython.

[micropython#12123: mpy_ld.py: Support complex RO sections](https://github.com/micropython/micropython/pull/12123).

This will hopefully be fixed in the coming months.



#### Build



Build the .mpy native module

```

make dist ARCH=armv6m MPY_DIR=../micropython

```



Install it on device

```

mpremote cp dist/armv6m*/emltrees.mpy :emltrees.mpy

```



#### Run tests



To build and run tests on host

```

make check

```



## Citations



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



```tex

@misc{emlearn_micropython,

  author       = {Jon Nordby},

  title        = {{emlearn-micropython: Efficient Machine Learning engine for MicroPython}},

  month        = aug,

  year         = 2023,

  doi          = {10.5281/zenodo.8212731},

  url          = {https://doi.org/10.5281/zenodo.8212731}

}

```