  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.
