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Previous issue date: 2012-08-07 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Artificial neural networks are usually applied to solve complex problems. In problems
with more complexity, by increasing the number of layers and neurons, it is possible to
achieve greater functional efficiency. Nevertheless, this leads to a greater computational
effort. The response time is an important factor in the decision to use neural networks in
some systems. Many argue that the computational cost is higher in the training period.
However, this phase is held only once. Once the network trained, it is necessary to use the
existing computational resources efficiently. In the multicore era, the problem boils down
to efficient use of all available processing cores. However, it is necessary to consider the
overhead of parallel computing. In this sense, this paper proposes a modular structure
that proved to be more suitable for parallel implementations. It is proposed to parallelize
the feedforward process of an RNA-type MLP, implemented with OpenMP on a shared
memory computer architecture. The research consistes on testing and analizing execution
times. Speedup, efficiency and parallel scalability are analyzed. In the proposed approach,
by reducing the number of connections between remote neurons, the response time of the
network decreases and, consequently, so does the total execution time. The time required
for communication and synchronization is directly linked to the number of remote neurons
in the network, and so it is necessary to investigate which one is the best distribution of
remote connections / As redes neurais artificiais geralmente s?o aplicadas ? solu??o de problemas comple-
xos. Em problemas com maior complexidade, ao aumentar o n?mero de camadas e de
neur?nios, ? poss?vel conseguir uma maior efici?ncia funcional, por?m, isto acarreta em
um maior esfor?o computacional. O tempo de resposta ? um fator importante na decis?o
de us?-las em determinados sistemas. Muitos defendem que o maior custo computacional
est? na fase de treinamento. Por?m, esta fase ? realizada apenas uma ?nica vez. J? trei-
nada, ? necess?rio usar os recursos computacionais existentes de forma eficiente. Diante
da era multicore esse problema se resume ? utiliza??o eficiente de todos os n?cleos de
processamento dispon?veis. No entanto, ? necess?rio considerar a sobrecarga existente na
computa??o paralela. Neste sentido, este trabalho prop?e uma estrutura modular que ?
mais adequada para as implementa??es paralelas. Prop?e-se paralelizar o processo feed-
forward (passo para frente) de uma RNA do tipo MLP, implementada com o OpenMP em
uma arquitetura computacional de mem?ria compartilhada. A investiga??o dar-se-? com
a realiza??o de testes e an?lises dos tempos de execu??o. A acelera??o, a efici?ncia e a es-
calabilidade s?o analisados. Na proposta apresentada ? poss?vel perceber que, ao diminuir
o n?mero de conex?es entre os neur?nios remotos, o tempo de resposta da rede diminui
e por consequ?ncia diminui tamb?m o tempo total de execu??o. O tempo necess?rio para
comunica??o e sincronismo est? diretamente ligado ao n?mero de neur?nios remotos da
rede, sendo ent?o, necess?rio observar sua melhor distribui??o
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/15447 |
| Date | 07 August 2012 |
| Creators | Souza, Francisco Ary Alves de |
| Contributors | CPF:82838607472, http://lattes.cnpq.br/9892239670106361, Martins, Allan de Medeiros, CPF:01979076448, http://lattes.cnpq.br/4402694969508077, Lopes, Danniel Cavalvante, CPF:02878120493, Souza, Samuel Xavier de |
| Publisher | Universidade Federal do Rio Grande do Norte, Programa de P?s-Gradua??o em Engenharia El?trica, UFRN, BR, Automa??o e Sistemas; Engenharia de Computa??o; Telecomunica??es |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Source | reponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN |
| Rights | info:eu-repo/semantics/openAccess |
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