TESTING MARS ROVER, SPECTRAL UNMIXING, AND SHIP DETECTION NEURAL NETWORKS, AND MEMORY CHECKERS ON EMBEDDED SYSTEMS ONBOARD THE ISS

Future space missions can benefit from processing imagery onboard to detect science events, create insights, and respond autonomously. This capability can enable the discovery of new science. One of the challenges to this mission concept is that traditional space flight hardware has limited capabilities and is derived from much older computing in order 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. They offer direct hardware acceleration for deep neural networks, which are state-of-the art in computer vision. 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 on these processors. The models are run multiple times on the ISS to see if any errors develop. In addition, we run a memory checker to detect radiation effects on the embedded processors.