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

  1. Carlos González Gutiérrez 1
  2. Jesús Daniel Santos Rodríguez 1
  3. Ramón Ángel Fernández Díaz 2
  4. José Luis Calvo Rolle 3
  5. Nieves Roqueñí Gutiérrez 1
  6. Francisco Javier de Cos Juez 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  2. 2 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

  3. 3 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

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Book:
International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings
  1. Manuel Graña Romay (ed. lit.)
  2. José Manuel López Guede (ed. lit.)
  3. Oier Etxaniz (ed. lit.)
  4. Álvaro Herrero Cosío (ed. lit.)
  5. Héctor Quintián Pardo (ed. lit.)
  6. Emilio Santiago Corchado Rodríguez (ed. lit.)

Publisher: Springer Suiza

ISBN: 978-3-319-47364-2

Year of publication: 2017

Pages: 279-289

Congress: International Conference on Computational Intelligence in Security for Information Systems (9. 2016. San Sebastián)

Type: Conference paper

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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.

Data source: Dialnet