Benchmarking and Testing Mars and Earth-based Deep Learning Classifiers and Memory Checkers on the Qualcomm Snapdragon and Intel Movidius Myriad X Processors Onboard the ISS

Future space missions can benefit from processing imagery onboard to detect science events, create insights, and respond autonomously. One of the challenges to this mission concept is that traditional space flight computing has limited capabilities because it is derived from much older computing to ensure reliable performance in the extreme environments of space, particularly radiation. Modern Commercial Off The Shelf (COTS) processors, such as the Movidius Myriad X and the Qualcomm Snapdragon, provide significant improvements 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 visual, synthetic aperture radar (SAR), or spectroscopic data from Earth or Mars, and standard deep learning models for image classification. Models are run multiple times, and memory checkers are deployed to test for radiation effects.