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

• Carlos González-Gutiérrez ^[1] ; Jesús Daniel Santos-Rodríguez ^[1] ; Ramón Ángel Fernández Díaz ^[2] ; Jose Luis Calvo Rolle ^[3] ; Nieves Roqueñí Gutiérrez ^[1] ; Cos Juez, Francisco Javier de ^[1]
  1. [1] Universidad de Oviedo

     Universidad de Oviedo

     Oviedo, España

     
  2. [2] Universidad de León

     Universidad de León

     León, España

     
  3. [3] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     

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• Localización: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings / coord. por Manuel Graña Romay , José Manuel López Guede , Oier Etxaniz, Álvaro Herrero Cosío , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2017, ISBN 978-3-319-47364-2, págs. 279-289
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.