21st EANN 2020, 5 -7 June 2020, Greece

Efficient Implementation of a Self-Sufficient Solar-Powered Real-Time Deep Learning-Based System

Sorin Liviu Jurj, Raul Rotar, Flavius Opritoiu, Mircea Vladutiu


  This paper presents a self-sufficient solar-powered real-time Deep Learning (DL) based system that runs inference 100% on solar energy and which is composed of an Nvidia Jetson TX2 board and a dual-axis solar tracker based on the cast-shadow principle. In order to have a higher energy being generat-ed by the solar tracker as well as a lower energy consumption by the real-time DL-based system, we have: a) updated our solar tracker’s panel with a higher number of polycrystalline photovoltaic (PV) cells and connected it to a chain of two inverters, one accumulator and one solar charge controller; b) implemented a motion detection method that triggers the inference process only when there is substantial movement in webcam frame. Experimental re-sults show that our solar tracker generates sufficient and constant solar ener-gy for all the 4 DL models (VGG-19, InceptionV3, ResNet-50 and Mo-bileNetV2) that are running in real-time on the Nvidia Jetson TX2 platform and which requires more than 5 times less energy when compared to a laptop having a Nvidia GTX 1060 GPU, proving that real-time DL-based systems can be powered by solar trackers without the need of traditional power plugs or need to pay for electricity bills.  

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