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

Autor:	Ramón Ángel Fernández Díaz, José Luis Calvo Rolle, Carlos González-Gutiérrez, Nieves Roqueñí Gutiérrez, Jesús Daniel Santos-Rodríguez, Francisco Javier de Cos Juez
Rok vydání:	2016
Předmět:	Speedup Artificial neural network Computer science business.industry Machine learning computer.software_genre 01 natural sciences 010309 optics Sight 0103 physical sciences Atmospheric turbulence Artificial intelligence business Adaptive optics 010303 astronomy & astrophysics computer
Zdroj:	International Joint Conference SOCO’16-CISIS’16-ICEUTE’16 ISBN: 9783319473635 SOCO-CISIS-ICEUTE
DOI:	10.1007/978-3-319-47364-2_27
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4d3ff6a62844a472f34f06e5d3690f60 https://doi.org/10.1007/978-3-319-47364-2_27 Zobrazit plný text záznamu