Dr. Cyril Cetre Profile

Dr. Cyril Cetre

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Proceedings Article | 27 August 2024 Presentation + Paper

Future-proof seamless real-time computing for AO with COSMIC

F. Ferreira, A. Sevin, J. Bernard, J. Plante, C. Cetre, N. Doucet, B. Pou, D. Gratadour

Proceedings Volume 13097, 130972B (2024) https://doi.org/10.1117/12.3020352

KEYWORDS: Adaptive optics, Real-time computing, Wavefronts, Machine learning, Telescopes

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The COSMIC platform endeavors to establish a potent, versatile, and accessible AO RTC platform for the AO community. This joint project allies the Observatoire de Paris with the Australian National University (ANU), and relies on the CACAO software, a creation of the Subaru Telescope. Although the primary intended use was for the ELT AO instrument, the platform now accommodates several systems on different scales. The effectiveness of COSMIC in current and future AO systems has already been proven. The current progress on the COSMIC platform extends beyond, with technical solutions offered for various applications other than AO such as embedded systems, radio astronomy, and radar. This interest exhibited by other communities has initiated new collaborations and additions to the platform, which might appeal to the AO community. This paper presents the current state of the COSMIC platform, including ongoing developments and future perspectives. One recent development is heterogeneous computation which allows for a hardware-agnostic real-time pipeline, thereby enabling the deployment of an AO pipeline on both CPU and GPU, irrespective of the vendor (NVIDIA or AMD). Another contribution involves the porting of COSMIC onto an NVIDIA Jetson Xavier, effectively paving the way for embedded system applications. As artificial intelligence is rapidly gaining importance in the AO community, efforts have been made to incorporate machine learning inference into the real-time critical path. Additionally, a collaboration with the radio astronomy community has led to a significant contribution towards efficient real-time data ingestion at a high volume. This paper aims to illustrate these achievements and demonstrate their potential applications within the AO community.

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