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. Jose Luis Calvo Rolle 3
  5. Nieves Roqueñí Gutiérrez 1
  6. Cos Juez, Francisco Javier de 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

Erakutsi afiliazioak +
Liburua:
International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings
  1. Manuel Graña (coord.)
  2. José Manuel López-Guede (coord.)
  3. Oier Etxaniz (coord.)
  4. Álvaro Herrero (coord.)
  5. Héctor Quintián (coord.)
  6. Emilio Corchado (coord.)

Argitaletxea: Springer Suiza

ISBN: 978-3-319-47364-2,  3-319-47364-6,  978-3-319-47363-5,  3-319-47363-8

Argitalpen urtea: 2017

Orrialdeak: 279-289

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

Mota: Biltzar ekarpena

DIALNET GOOGLE SCHOLAR

Laburpena

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.

Datuen iturria: Dialnet