GPU boosted CNN simulator library for graphical flow-based programmability

  • Authors:

  • Balázs Gergely Soós;Ádám Rák;József Veres;György Cserey

  • Affiliations:

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

  • Venue:
  • EURASIP Journal on Advances in Signal Processing - CNN technology for spatiotemporal signal processing
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

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. UniversalMachine 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.