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Open Access

GPU Boosted CNN Simulator Library for Graphical Flow-Based Programmability

  • Balázs Gergely Soós1, 2Email author,
  • Ádám Rák1,
  • József Veres1 and
  • György Cserey3
EURASIP Journal on Advances in Signal Processing20092009:930619

Received: 2 October 2008

Accepted: 12 March 2009

Published: 26 April 2009


A graphical environment for CNN algorithm development is presented. The new generation of graphical cards with many general purpose processing units introduces the massively parallel computing into PC environment. Universal Machine on Flows- (UMF) like notation, highlighting image flows and operations, is a useful tool to describe image processing algorithms. This documentation step can be turned into modeling using our framework backed with MATLAB Simulink and the power of a video card. This latter relatively cheap extension enables a convenient and fast analysis of CNN dynamics and complex algorithms. Comparison with other PC solutions is also presented. For single template execution, our approach yields run times 40x faster than that of the widely used Candy simulator. In the case of simpler algorithms, real-time execution is also possible.


CandyImage FlowImage Processing AlgorithmGraphical CardPublisher Note

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Authors’ Affiliations

Faculty of Information Technology, Pázmány Péter Catholic University, Budapest, Hungary
Computer and Automation Research Institute of the Hungarian Academy of Sciences, Budapest, Hungary
Infobionic and Neurobiological Plasticity Research Group, Hungarian Academy of Sciences, Pázmány University and Semmelweis University, Budapest, Hungary


© Balázs Gergely Soós et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.