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GPU@SAT: A General-Purpose Programmable Accelerator for on Board Data Processing and Satellite Autonomy

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The Use of Artificial Intelligence for Space Applications (AII 2022)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1088))

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Abstract

In the last decade, Artificial Intelligence (AI) algorithms are becoming very influential in several sectors thanks to their flexibility and adaptability. Among the several fields of the AI, the most attractive and with the greatest results is Machine Learning (ML). It aims to extract and elaborating characteristics starting from a huge amount of data. Space missions could strongly benefit of these algorithms, allowing the elaboration and processing of data directly where they are acquired. Thanks to the technological advancements in designing low-power hardware accelerators, several enterprises (both private and public) have used Components Off-The-Shelf (COTS) hardware accelerators to run their on-the-edge applications. This has allowed to mitigate the server Graphical Processing Unit (GPU) high power consumption and overheating while maintaining good performance. However, these devices cannot be used in the harsh space environment, due to their low radiation tolerance, that is a fundamental requirement for safely working in space. GPU@SAT aims to revolutionise the current Artificial Intelligence execution and signal processing analysis, exploiting a general-purpose GPU-like IP-core fitted on a radiation-hardened FPGA. The board will be adopted in the framework of a European Space Agency (ESA) project under the Advanced Research in TElecommunications Systems (ARTES) programme. AIKO, the company leader of this activity, is developing AI algorithms for Fault Detection Isolation and Recovery systems. The adoption of such algorithms, executed via dedicated and space-qualified systems, directly on-board of satellites, can strongly increase the life of the mission, while reducing the design costs.

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References

  1. Coral. https://coral.ai/

  2. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 265–283 (2016)

    Google Scholar 

  3. Barry, B., Brick, C., Connor, F., Donohoe, D., Moloney, D., Richmond, R., O’Riordan, M., Toma, V.: Always-on vision processing unit for mobile applications. IEEE Micro 35(2), 56–66 (2015)

    Article  Google Scholar 

  4. Cass, S.: Nvidia makes it easy to embed AI: The Jetson nano packs a lot of machine-learning power into DIY projects - [hands on]. IEEE Spectr. 57(7), 14–16 (2020). https://doi.org/10.1109/MSPEC.2020.9126102

    Article  Google Scholar 

  5. Dakir, A., Barramou, F., Alami, O.B.: Opportunities for artificial intelligence in precision agriculture using satellite remote sensing. In: Geospatial Intelligence, pp. 107–117. Springer (2022)

    Google Scholar 

  6. ESA/ESTEC: SAVOIR FDIR Handbook 2.0 – SAVOIR-HB-003 (2019)

    Google Scholar 

  7. George, A.D., Wilson, C.M.: Onboard processing with hybrid and reconfigurable computing on small satellites. Proc. IEEE 106(3), 458–470 (2018). https://doi.org/10.1109/JPROC.2018.2802438

    Article  Google Scholar 

  8. Giuffrida, G., Diana, L., de Gioia, F., Benelli, G., Meoni, G., Donati, M., Fanucci, L.: Cloudscout: a deep neural network for on-board cloud detection on hyperspectral images. Remote Sens. 12(14), 2205 (2020)

    Article  Google Scholar 

  9. Jacobsen, R., Bernabel, C.A., Hobbs, M., Oishi, N., Puig-Hall, M., Zirbel, S.: Machine learning: paving the way for more efficient disaster relief. In: AIAA SCITECH 2022 Forum, p. 0397 (2022)

    Google Scholar 

  10. Kothari, V., Liberis, E., Lane, N.D.: The final frontier: deep learning in space. In: Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications, pp. 45–49 (2020)

    Google Scholar 

  11. Lattner, C.: LLVM and Clang: next generation compiler technology. In: The BSD Conference, vol. 5 (2008)

    Google Scholar 

  12. Lattner, C., Adve, V.: Llvm: a compilation framework for lifelong program analysis & transformation. In: International Symposium on Code Generation and Optimization, 2004. CGO 2004, pp. 75–86. IEEE (2004)

    Google Scholar 

  13. Lattner, C., Adve, V.: LLVM: a compilation framework for lifelong program analysis and transformation, pp. 75–88. San Jose, CA, USA (Mar 2004)

    Google Scholar 

  14. Lattner, C., Amini, M., Bondhugula, U., Cohen, A., Davis, A., Pienaar, J., Riddle, R., Shpeisman, T., Vasilache, N., Zinenko, O.: Mlir: a compiler infrastructure for the end of Moore’s law. arXiv preprint arXiv:2002.11054 (2020)

  15. Liu, R., Yang, B., Zio, E., Chen, X.: Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech. Syst. Signal Process. 108, 33–47 (2018)

    Article  Google Scholar 

  16. Meß, J.G., Dannemann, F., Greif, F.: Techniques of artificial intelligence for space applications-a survey. In: European Workshop on On-Board Data Processing (OBDP2019). European Space Agency (2019)

    Google Scholar 

  17. Munshi, A.: The opencl specification. In: 2009 IEEE Hot Chips 21 Symposium (HCS), pp. 1–314. IEEE (2009)

    Google Scholar 

  18. Murphy, J., Ward, J.E., Mac Namee, B.: Machine learning in space: a review of machine learning algorithms and hardware for space applications. AICS, pp. 72–83 (2021)

    Google Scholar 

  19. Ortiz-Gomez, F.G., Lei, L., Lagunas, E., Martinez, R., Tarchi, D., Querol, J., Salas-Natera, M.A., Chatzinotas, S.: Machine learning for radio resource management in multibeam geo satellite systems. Electronics 11(7), 992 (2022)

    Article  Google Scholar 

  20. Paakko, M., Myllymäki, P., Holsti, N., Tirri, H.: Bayesian networks for advanced FDIR. In: ESA Workshop on On-Board Autonomy, Noordwijk, The Netherlands (2001)

    Google Scholar 

  21. Rodriguez-Andina, J.J., Moure, M.J., Valdes, M.D.: Features, design tools, and application domains of FPGAs. IEEE Trans. Ind. Electron. 54(4), 1810–1823 (2007)

    Article  Google Scholar 

  22. Sabne, A.: XLA: compiling machine learning for peak performance (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. IngeniArs S.r.l., Via Ponte a Piglieri 8, 56122, Pisa, Italy

    Roberto Ciardi, Gianluca Giuffrida & Gionata Benelli

  2. AIKO SRL, Via dei Mille 22, 10123, Torino, Italy

    Christian Cardenio & Riccardo Maderna

Authors
  1. Roberto Ciardi
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  2. Gianluca Giuffrida
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  3. Gionata Benelli
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  4. Christian Cardenio
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  5. Riccardo Maderna
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Corresponding author

Correspondence to Roberto Ciardi .

Editor information

Editors and Affiliations

  1. Reggio Calabria, Italy

    Cosimo Ieracitano

  2. Reggio Calabria, Italy

    Nadia Mammone

  3. Rome, Italy

    Marco Di Clemente

  4. Nottingham, UK

    Mufti Mahmud

  5. Tucson, AZ, USA

    Roberto Furfaro

  6. REGGIO CALABRIA, Reggio Calabria, Italy

    Francesco Carlo Morabito

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Ciardi, R., Giuffrida, G., Benelli, G., Cardenio, C., Maderna, R. (2023). GPU@SAT: A General-Purpose Programmable Accelerator for on Board Data Processing and Satellite Autonomy. In: Ieracitano, C., Mammone, N., Di Clemente, M., Mahmud, M., Furfaro, R., Morabito, F.C. (eds) The Use of Artificial Intelligence for Space Applications. AII 2022. Studies in Computational Intelligence, vol 1088. Springer, Cham. https://doi.org/10.1007/978-3-031-25755-1_3

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