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

arXiv: https://arxiv.org/abs/2106.07561 · HTML: https://mlsanity.com/paper/2106.07561/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 1 of 1)

- [Fully Embedding Fast Convolutional Networks on Pixel Processor Arrays](https://mlsanity.com/paper/2004.12525/) (2020)

## Built on by (most cited first, 1 of 1)

- [On-Sensor Binarized Fully Convolutional Neural Network with A Pixel Processor Array](https://mlsanity.com/paper/2202.00836/) (2022)

## Abstract (arXiv)

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.