Bringing Power-Efficient AI Applications to the Edge
Learn how to move AI closer to the Edge using sensors with Machine Learning Core
This webinar was broadcast on Thursday, August 19th, 2021
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 1-hour, 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 development tools.
You will 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 the examples available in ST’s online repository
- How to leverage the ST BLE Sensor App and the SensorTile.box when developing with the MLC
- How Qeexo, a partner in ST’s ecosystem, supports MLC through integrated toolbox options and consulting services
Thiago Reis is a Product Marketing Engineer at STMicroelectronics with broad experience in IoT platforms for the industrial, consumer and automotive industries. Joining ST in 2013, he closely follows innovative applications enhanced by ST's sensors and RF product offerings. Thiago has worked extensively in system-level design and has played a key role in many success stories benefiting from ST's Open Development Environment.