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arXiv · 2106.07561

Direct Servo Control from In-Sensor CNN Inference with A Pixel Processor Array

Yanan Liu·Jianing Chen·Laurie Bose·Piotr Dudek·Walterio Mayol-Cuevas
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Abstract

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.

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Yanan Liu, Jianing Chen, Laurie Bose, Piotr Dudek, Walterio Mayol-Cuevas. 2021-05-26. Direct Servo Control from In-Sensor CNN Inference with A Pixel Processor Array. https://arxiv.org/abs/2106.07561

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