A Sensor with an embedded Machine Learning Core (MLC) can improve the overall system efficiency by significantly reducing power consumption while increasing accuracy with contextual awareness. The Machine Learning processing capability allows moving some algorithms from the host processor to the Inertial Measurement Unit (IMU). The IMU would therefore only consume a fraction of the MCU power used for the same typical tasks. The MLC is designed to run in a highly power-efficient manner and provides accurate results in the shortest possible time.
In this on-demand webinar, you will learn how to benefit from the classification engine on the Machine Learning Core embedded in an iNEMO™ inertial module, and how to quickly and easily design power-efficient decision trees using the ST’s development tools.
You will also learn:
- How to program a sensor with a Machine Learning Core without writing a single line of code
- The 5 key steps behind Machine Learning with practical examples using Sensors
- How to use and benefit from a series of examples available in our online repository
- How to leverage the ST BLE Sensor App and the SensorTile.box when developing with the MLC