Quantized deep learning models on low-power edge devices for robotic systems
Signal Processing
2019-12-03 v1 Machine Learning
Abstract
In this work, we present a quantized deep neural network deployed on a low-power edge device, inferring learned motor-movements of a suspended robot in a defined space. This serves as the fundamental building block for the original setup, a robotic system for farms or greenhouses aimed at a wide range of agricultural tasks. Deep learning on edge devices and its implications could have a substantial impact on farming systems in the developing world, leading not only to sustainable food production and income, but also increased data privacy and autonomy.
Keywords
Cite
@article{arxiv.1912.00186,
title = {Quantized deep learning models on low-power edge devices for robotic systems},
author = {Anugraha Sinha and Naveen Kumar and Murukesh Mohanan and MD Muhaimin Rahman and Yves Quemener and Amina Mim and Suzana Ilić},
journal= {arXiv preprint arXiv:1912.00186},
year = {2019}
}
Comments
Presented at NeurIPS 2019 Workshop on Machine Learning for the Developing World