An On-board Algorithm Implementation on an Embedded GPU: A Space Case Study

Abstract

On-board processing requirements of future space missions are constantly increasing, calling for new hardware than the traditional ones used in space. Embedded GPUs are an attractive candidate offering both high performance capabilities and low power consumption, but there are no complex industrial case studies from the space domain demonstrating these advantages. In this Master Thesis we present the GPU parallelization of an on-board algorithm in multiple GPU programming languages, as well as its performance and energy efficiency on a selection of promising embedded GPU COTS platforms.