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

Anugraha Sinha; Naveen Kumar; Murukesh Mohanan; MD Muhaimin Rahman; Yves Quemener; Amina Mim; Suzana Ilić

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arXiv · 1912.00186

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

Anugraha Sinha·Naveen Kumar·Murukesh Mohanan·MD Muhaimin Rahman·Yves Quemener·Amina Mim·Suzana Ilić

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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.

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Anugraha Sinha, Naveen Kumar, Murukesh Mohanan, MD Muhaimin Rahman, Yves Quemener, Amina Mim, Suzana Ilić. 2019-11-30. Quantized deep learning models on low-power edge devices for robotic systems. https://arxiv.org/abs/1912.00186

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