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.
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. 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.
This repository is structured as follows:
- An application_examples folder, containing examples of applications ready to be used with the sensor.
- A configuration_examples folder, containing examples of configurations using different ST hardware and software tools.
- A tools folder, containing some additional scripts for decision tree generation.
More information: http://www.st.com
Copyright © 2021 STMicroelectronics