Evaluating the computational capabilities of embedded multicore and GPU platforms for on-board image processing

On-board Image Processing is one of the most computationally intensive tasks performed in space payload processing. With the constant increase in the resolution of optical sensors, new powerful architectures are needed for their processing. In this work, we evaluate the capabilities of several state-of-the-art embedded COTS and radiation tolerant multicore and GPU featuring platforms, with an open source on-board image processing application we have developed and parallelised. Our results show that both embedded multicores and especially GPUs are very effective for such tasks. In fact, GPUs become even more efficient when sensor sizes are increasing, making them ideal candidates for future space missions. ; This work was supported by ESA through the 4000136514/21/NL/GLC/my co-funded PhD activity ”Mixed Software/Hardware-based Fault-tolerance Techniques for Complex COTS System-on-Chip in Radiation Environments” and the GPU4S (GPU for Space) ESA-funded project. Moreover, it was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772773) and European Community’s Horizon Europe programme under the METASAT project (grant agreement 101082622). In addition, it was partially supported by the Spanish Ministry of Economy and Competitiveness under grants PID2019-107255GB-C21 and IJC-2020-045931-I (Spanish State Research Agency / Agencia Espanola de Investigacion (AEI) / http://dx.doi.org/10.13039/501100011033) and by the Department of Research and Universities of the Government of Catalonia with a grant to the CAOS Research Group (Code: 2021 SGR 00637). ; Peer Reviewed ; Postprint (author's final draft)