Power consumption is the major concern in designing a battery-operated camera system that interprets images on an edge sensor. Greenwaves’ GAP application processors enable new types of devices that combine ultra-low power consumption with sophisticated signal processing and neural network algorithms.
In this post, we demonstrate how to train and deploy a deep learning model for image recognition on GAP8—the first generation of ultra-low power IoT application processors. Thanks to the power-optimized MCU-class architecture tailored for intensive AI workloads, GAP8 is the perfect solution when coupled with low-power cameras.
A GAP8-based smart camera that leverages Convolutional Neural Networks MobilenetV2 can process image data while consuming less than 37.5 mW/FPS.
In this post, you will learn the necessary design steps to build a GAP8-based smart camera system from data to prototype in a few hours. After training and converting a model using the TensorFlow toolkit, the GAPflow toolset is exploited to bring trained models on the GAP8 chip.
You can also find more information in our NN Menu—GreenWaves’ repository, which contains common mobile and edge NN architecture examples, NN sample applications, and full flagged reference designs. Our tools map a TFLlite model (quantized or unquantized) onto Gap.