Reconfigurable hardware architecture of a shape recognition system based on specialized tiny neural networks with online training.

Moreno González, Félix Antonio; Alarcón, Jaime; Salvador Perea, Rubén y Riesgo Alcaide, Teresa (2009). Reconfigurable hardware architecture of a shape recognition system based on specialized tiny neural networks with online training.. "IEEE Transactions on Industrial Electronics.", v. 56 (n. 8); pp. 3253-3263. ISSN 0278-0046. https://doi.org/10.1109/TIE.2009.2022076.

Descripción

Título: Reconfigurable hardware architecture of a shape recognition system based on specialized tiny neural networks with online training.
Autor/es:
  • Moreno González, Félix Antonio
  • Alarcón, Jaime
  • Salvador Perea, Rubén
  • Riesgo Alcaide, Teresa
Tipo de Documento: Artículo
Título de Revista/Publicación: IEEE Transactions on Industrial Electronics.
Fecha: Agosto 2009
Volumen: 56
Materias:
Palabras Clave Informales: Neural network hardware implementation, run-time learning, recognition.
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Automática, Ingeniería Electrónica e Informática Industrial [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Neural networks are widely used in pattern recognition, security applications, and robot control. We propose a hardware architecture system using tiny neural networks (TNNs)specialized in image recognition. The generic TNN architecture allows for expandability by means of mapping several basic units(layers) and dynamic reconfiguration, depending on the application specific demands. One of the most important features of TNNs is their learning ability. Weight modification and architecture reconfiguration can be carried out at run-time. Our system performs objects identification by the interpretation of characteristics elements of their shapes. This is achieved by interconnecting several specialized TNNs. The results of several tests in different conditions are reported in this paper. The system accurately detects a test shape in most of the experiments performed. This paper also contains a detailed description of the system architecture and the processing steps. In order to validate the research, the system has been implemented and configured as a perceptron network with back-propagation learning, choosing as reference application the recognition of shapes. Simulation results show that this architecture has significant performance benefits.

Más información

ID de Registro: 5084
Identificador DC: http://oa.upm.es/5084/
Identificador OAI: oai:oa.upm.es:5084
Identificador DOI: 10.1109/TIE.2009.2022076
URL Oficial: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04926188
Depositado por: Memoria Investigacion
Depositado el: 25 Nov 2010 11:35
Ultima Modificación: 23 Feb 2017 17:32
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