GPU powered CNN simulator (SIMCNN) with graphical flow based programmability
Fac. of Inf. Technol., Pazmany Peter Catholic Univ., Budapest
Cellular Neural Networks and Their Applications, 2008. CNNA 2008. 11th International Workshop on In Cellular Neural Networks and Their Applications, 2008. CNNA 2008. 11th International Workshop on (2008), pp. 163-168.
@conference{soos2008gpu,
title={GPU powered CNN simulator (SIMCNN) with graphical flow based programmability},
author={Soos, B.G. and Rak, A. and Veres, J. and Cserey, G.},
booktitle={Cellular Neural Networks and Their Applications, 2008. CNNA 2008. 11th International Workshop on},
pages={163–168},
year={2008},
organization={IEEE}
}
In this paper, we introduce an innovative CNN algorithm development environment that significantly assists algorithmic design. The introduced graphical user interface uses Matlab Simulink with UMF-like program description, where direct functionality accompanies better accessability. The new generation of graphical cards incorporate many general purpose graphics processing units, giving the power of parallel computing to a simple PC environment cheaply. Therefore, analysis of CNN dynamics become more feasible with a common hardware setup. Our measurements demonstrate the efficiency of the realized system. In the case of simpler algorithms, real-time execution is also possible.
November 2, 2010 by hgpu
