Benchmarking Deep Learning Inference of Remote Sensing Imagery on the Qualcomm Snapdragon And Intel Movidius Myriad X Processors Onboard the International Space Station

Emily Dunkel, Jason Swope, Zaid J. Towfic, Steve A. Chien, Damon Russell, Joseph Sauvageau, Douglas Sheldon, Juan Romero-Cañas, José Luis Espinosa-Aranda, Léonie Buckley, Elena Hervas-Martin, Mark R. Fernandez, Carrie Knox

Abstract: Deep space missions can benefit from onboard image analysis. We demonstrate deep learning inference to facilitate such analysis for future mission adoption. Traditional space flight hardware provides modest compute when compared to today's laptop and desktop computers. New generations of commercial off the shelf (COTS) processors designed for embedded applications, such as the Qualcomm Snapdragon and Movidius Myriad X, deliver significant compute in small Size Weight and Power (SWaP) packaging and offer direct hardware acceleration for deep neural networks. We deploy neural network models on these processors hosted by Hewlett Packard Enterprise's Spaceborne Computer-2 onboard the International Space Station (ISS). We benchmark a variety of algorithms trained on imagery from Earth or Mars, as well as some standard deep learning models for image classification.