Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems
AbstractMany of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named “CARMEN” are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances. View Full-Text
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González-Gutiérrez, C.; Santos, J.D.; Martínez-Zarzuela, M.; Basden, A.G.; Osborn, J.; Díaz-Pernas, F.J.; De Cos Juez, F.J. Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems. Sensors 2017, 17, 1263.
González-Gutiérrez C, Santos JD, Martínez-Zarzuela M, Basden AG, Osborn J, Díaz-Pernas FJ, De Cos Juez FJ. Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems. Sensors. 2017; 17(6):1263.Chicago/Turabian Style
González-Gutiérrez, Carlos; Santos, Jesús D.; Martínez-Zarzuela, Mario; Basden, Alistair G.; Osborn, James; Díaz-Pernas, Francisco J.; De Cos Juez, Francisco J. 2017. "Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems." Sensors 17, no. 6: 1263.
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