Performance study of software AER-based convolutions on a parallel supercomputer

Authors:
Rafael J. Montero-Gonzalez;Arturo Morgado-Estevez;Alejandro Linares-Barranco;Bernabe Linares-Barranco;Fernando Perez-Peña;Jose Antonio Perez-Carrasco;Angel Jimenez-Fernandez
Affiliations:
Applied Robotics Research Lab, Engineering School, University of Cadiz, Spain;Applied Robotics Research Lab, Engineering School, University of Cadiz, Spain;Robotic and Technology of Computers Lab, University of Seville, Spain;Institute of Microelectronics of Seville, IMSE-CNM-CSIC, Spain;Applied Robotics Research Lab, Engineering School, University of Cadiz, Spain;Institute of Microelectronics of Seville, IMSE-CNM-CSIC, Spain;Robotic and Technology of Computers Lab, University of Seville, Spain
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Year:
2011

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Abstract

This paper is based on the simulation of a convolution model for bioinspired neuromorphic systems using the Address-Event-Representation (AER) philosophy and implemented in the supercomputer CRS of the University of Cadiz (UCA). In this work we improve the runtime of the simulation, by dividing an image into smaller parts before AER convolution and running each operation in a node of the cluster. This research involves a test cases design in which the optimal parameters are set to run the AER convolution in parallel processors. These cases consist on running the convolution taking an image divided in different number of parts, applying to each part a Sobel filter for edge detection, and based on the AER-TOOL simulator. Execution times are compared for all cases and the optimal configuration of the system is discussed. In general, CRS obtain better performances when the image is divided than for the whole image.