This work demonstrates direct visual sensory-motor control using high-speed CNN inference via a SCAMP-5 Pixel Processor Array (PPA). We demonstrate how PPAs are able to efficiently bridge the gap between perception and action. A binary Convolutional Neural Network (CNN) is used for a classic rock, paper, scissors classification problem at over 8000 FPS. Control instructions are directly sent to a servo motor from the PPA according to the CNN's classification result without any other intermediate hardware.
Cite
@article{arxiv.2106.07561,
title = {Direct Servo Control from In-Sensor CNN Inference with A Pixel Processor Array},
author = {Yanan Liu and Jianing Chen and Laurie Bose and Piotr Dudek and Walterio Mayol-Cuevas},
journal= {arXiv preprint arXiv:2106.07561},
year = {2021}
}