# STMems_Machine_Learning_Core

**Repository Path**: mirrors_STMicroelectronics/STMems_Machine_Learning_Core

## Basic Information

- **Project Name**: STMems_Machine_Learning_Core
- **Description**: Configuration files, examples and tools for the Machine Learning Core feature (MLC) available in STMicroelectronics MEMS sensors. Some examples of devices including MLC: LSM6DSOX, LSM6DSRX, ISM330DHCX, IIS2ICLX, LSM6DSO32X, ASM330LHHX, LSM6DSV16X, LIS2DUX12, LIS2DUXS12, LSM6DSV16BX, ASM330LHHXG1, LSM6DSV32X
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-18
- **Last Updated**: 2025-09-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Important Notice
==================

DISCONTINUED (July 2025): the maintenance for this repository has been discontinued.

Please refer to:

https://github.com/STMicroelectronics/st-mems-machine-learning-core/

for the up-to-date tutorials, examples and tools for the Machine Learning Core (MLC) feature available in STMicroelectronics MEMS sensors.

# Machine Learning Core

This repository provides information, examples and configurations of the **Machine Learning Core** (MLC), a hardware processing engine dedicated to the most extreme real-time edge computing available in the latest  products in the [**ST sensors portfolio**](https://www.st.com/en/mems-and-sensors.html?sc=MEMS). Products that offer the MLC end in "X".

Machine Learning processing allows moving some algorithms from the application processor to the *STMicroelectronics* sensor, enabling consistent reduction of power consumption.

Machine Learning processing is obtained through *decision-tree logic*. A decision tree is a mathematical tool composed of a series of configurable *nodes*. Each *node* is characterized by an *“if-then-else”* condition, where an input signal (represented by statistical parameters calculated from the sensor data) is evaluated against a threshold.

The results of the decision tree can be read from the application processor at any time. Furthermore, there is the possibility to **generate an interrupt** for every change in the result in the decision tree.

For more information about MLC, please explore the dedicated page available on the ST website: [MEMS Sensors Ecosystem for Machine Learning](https://www.st.com/content/st_com/en/MEMS-Sensors-Ecosystem-for-Machine-Learning.html).


## Repository overview

This repository is structured as follows:  

- An [application_examples](./application_examples/) folder, containing examples of applications ready to be used with the sensor.
- A [configuration_examples](./configuration_examples/) folder, containing examples of configurations using different ST hardware and software tools.
- A [tools](./tools/) folder, containing some additional scripts for decision tree generation.

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**More information: [http://www.st.com](http://st.com/MEMS)**

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