Israel Tabarez-Paz1, Neil Hernandez-Gress2 and Miguel Gonzalez Mendoza2, 1Universidad Autonoma del Estado de Mexico, Mexico and 2Campus Estado de Mexico, Mexico
ABSTRACT
In this paper we study the performance of Spiking Neural Networks (SNN)and Support Vector Machine (SVM) by using a GPU, model GeForce 6400M. Respect to applications of SNN, the methodology may be used for clustering, classification of databases, odor, speech and image recognition..In case of methodology SVM, is typically applied for clustering, regression and progression. According to particular characteristics of these methodologies,theycan be parallelizedin several grades. However, level of parallelism is limited to architecture of hardware. So, is very sure to get better results using other hardware with more computational resources. The different approaches are evaluated by the training speed and performance. On the other hand, some authors have coded algorithms SVM light, but nobody has programming QP SVM in a GPU. Algorithms were coded by authors in the hardware, like Nvidia card, FPGA or sequential circuits that depends on methodology used, to compare learning timewith between GPU and CPU. Also, in the survey we introduce a brief description of the types of ANN and its techniques of execution to be related with results of researching.
KEYWORDS
GPU, Spiking Neural Networks, Support Vector Machines, pattern recognition.
Original Source URL: http://airccse.org/journal/ijsc/papers/4313ijsc01.pdf
https://airccse.org/journal/ijsc/current2013.html
#Fuzzysystems #Softcomputing #Neuralnetworks #Wavelet #Patternrecognition #Machinelearning
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