In this session, we’ll introduce the OpenMV AE3 and OpenMV N6 low-power, high-performance embedded machine vision cameras, which are 200x better than our previous generation systems. We’ll show how you can run YOLO at 25 fps on the OpenMV AE3 while drawing less than 0.25 W. We’ll also explain how the OpenMV AE3 can go into deep sleep mode on demand to draw less than 0.25 mW, allowing you to create a smart machine vision camera that can run on batteries for years. We will present how you can leverage TensorFlow to run accelerated CNNs on these cameras and implement pre- and post-processing using MicroPython and Numpy. Finally, we’ll show how you can use OpenAMP with MicroPython running on the camera to leverage dual-core heterogeneous processing and enable always-on NPU accelerated AI sensing.