Quantized Deep Learning Models on Low-Power Edge Devices for Robotic Systems.
CoRR(2019)
摘要
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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关键词
Deep Learning,Distributed Control,Agricultural Robotics,Smart Farming
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