A SURVEY OF SPIKING NEURAL NETWORKS AND
SUPPORT VECTOR MACHINE PERFORMANCE
BYUSINGGPU’S
Israel Tabarez-Paz1
, Neil Hernández-Gress2
and Miguel González Mendoza2
.
1Universidad Autónomadel Estado de México
Blvd. Universitario s/n, Predio San Javier Atizapán de Zaragoza,
México 2
Tecnológico de Monterrey, Campus Estado de México,
CarreteraLago de Guadalupe km 3.5 Atizapán de Zaragoza Col. Margarita Maza de
Juarez, Atizapán de Zaragora, México
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
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