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2017 | OriginalPaper | Buchkapitel

Using GPUs to Speed up a Tomographic Reconstructor Based on Machine Learning

verfasst von : Carlos González-Gutiérrez, Jesús Daniel Santos-Rodríguez, Ramón Ángel Fernández Díaz, Jose Luis Calvo Rolle, Nieves Roqueñí Gutiérrez, Francisco Javier de Cos Juez

Erschienen in: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16

Verlag: Springer International Publishing

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Abstract

The next generation of adaptive optics (AO) systems require tomographic techniques in order to correct for atmospheric turbulence along lines of sight separated from the guide stars. Multi-object adaptive optics (MOAO) is one such technique. Here we present an improved version of CARMEN, a tomographic reconstructor based on machine learning, using a dedicated neural network framework as Torch. We can observe a significant improvement on the training an execution times of the neural network, thanks to the use of the GPU.

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Titel: Using GPUs to Speed up a Tomographic Reconstructor Based on Machine Learning
verfasst von: Carlos González-Gutiérrez
Jesús Daniel Santos-Rodríguez
Ramón Ángel Fernández Díaz
Jose Luis Calvo Rolle
Nieves Roqueñí Gutiérrez
Francisco Javier de Cos Juez
Verlag: Springer International Publishing
Buch: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16
Print ISBN: 978-3-319-47363-5

Electronic ISBN: 978-3-319-47364-2

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-47364-2_27

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