Machine Learning for embedded systems has recently started to make sense: on-device inference reduces latency, costs and minimizes power consumption compared to cloud-based solutions. Thanks to Google TFLite Micro, and its optimized ARM CMSIS NN kernel, on-device inference now also means microcontrollers such as ARM Cortex-M processors.
In this session, we will examine machine vision examples running on the small and power-efficient OpenMV Cam H7. Attendees will learn what it takes to train models with popular desktop Machine Learning frameworks and deploy them to a microcontroller. We will take a hands-on approach, using the OpenMV board to run the inference and detect objects placed in front of the camera.