Análise de técnicas de implementação paralela para treinamento de redes neurais em GPU

• Ano de defesa: 2014
• Autor(a) principal: Gurgel, Sáskya Thereza Alves
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraí­ba; BR; Informática; Programa de Pós Graduação em Informática; UFPB
• Palavras-chave em Português: Redes Neurais; computação paralela; GPU; Neural Networks; parallel computation; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/6117

With the increase of data volume and the latent necessity of turn them into knowledge and information, arises the need to develop techniques able to perform the data analysis in a timely and efficient manner. Neural networks promotes an data analysis that is able to classify and predict information. However, the natural model of parallel computing proposed by neural networks, requires techniques of implementation with high processing power. The evolution of parallel hardware provides an environment with ever growing computational power. The GPU is a hardware that is able to process parallel implementations in a efficient way and at low cost. Therefore, this paper provides a technique of parallel implementation of neural networks with GPU processing and seeks to achieve an comparative analysis between different implementation techniques found in literature and the technique proposed in this paper.