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emlearn-micropython

Micropython integration for the emlearn 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 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

• Tested working on x64 (Unix port) and xtensawin (ESP32).

NOTE: Building for ARM, such as armv6m (Cortex M0 / RP2040) requires a patch to MicroPython (issue).

Features

• Classification with RandomForest/DecisionTree models
• Classification and on-device learning with K-Nearest Neighbors (KNN)
• Classification with Convolutional Neural Network (CNN), using TinyMaix library.
• Fast Fourier Transform (FFT) for feature preprocessing, or general DSP
• Infinite Impulse Response (IIR) filters for feature preprocessing, or general DSP
• Clustering using K-means
• Scaling and data type transformations for array, using emlearn_arrayutils.
• Load/save Numpy .npy files using micropython-npyfile
• Installable as a MicroPython native module. No rebuild/flashing needed
• Operates on standard array.array data structures
• Models can be loaded at runtime from a file in disk/flash
• Highly efficient. Inference times down to 100 microseconds, RAM usage <2 kB, FLASH usage <2 kB
• Pre-built binaries available for most architectures.

Examples

• xor_trees. A "Hello World", using RandomForest.
• mnist_cnn. Basic image classification, using Convolutional Neural Network.
• har_trees. Accelerometer-based Human Activity Recognition, using Random Forest
• soundlevel_iir. Sound Level Meter, using Infinite Impulse Response (IIR) filters.

Prerequisites

Minimally you will need

• Python 3.10+ on host
• MicroPython 1.23+ running onto your device

Download repository

Download the repository with examples etc

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

Usage

Start with the instructions in XOR example.

Supported versions

At any given point in time, emlearn-micropython only provides pre-built binaries for one MicroPython version. In general we strongly encourage people to use the latest version. There are no long-term-support or bugfix versions, at this point. If you build from source, the current version of emlearn-micropython might also work on a couple of MicroPython versions around the time, but this is not guaranteed.

Find architecture and .mpy version

The correct .mpy files to use depend on the CPU architecture of your microcontroller, as well as the MicroPython version.

Identify which CPU architecture your device uses. You need to specify ARCH to install the correct module version.

Information is also available in the official documentation: MicroPython: .mpy files

Benchmarks

UCI handwriting digits

UCI ML hand-written digits datasets dataset from sklearn.datasets.load_digits. 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).

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.

We assume that micropython is installed in the same place as this repository. If using another location, adjust MPY_DIR accordingly.

You should be using the latest MicroPython 1.23 (or newer).

Build

Build the .mpy native module

make dist ARCH=armv6m MPY_DIR=../micropython

Install it on device

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

@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}
}