Quantized deep learning models on low-power edge devices for robotic systems

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 ran...

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Bibliographic Details
Published in:arXiv.org 2019-11
Main Authors: Sinha, Anugraha, Kumar, Naveen, Mohanan, Murukesh, Rahman, MD Muhaimin, Quemener, Yves, Mim, Amina, Ilić, Suzana
Format: Article
Language:English
Subjects:
Deep learning
Farms
Machine learning
Neural networks
Online Access:Get full text
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Summary: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.
ISSN:2331-8422