Approach to an FPGA embedded, autonomous object recognition system: run-time learning and adaptation

Salvador Perea, Rubén; Terleira, Carlos; Moreno González, Félix Antonio y Riesgo Alcaide, Teresa (2009). Approach to an FPGA embedded, autonomous object recognition system: run-time learning and adaptation. En: "VLSI Circuits and Systems IV", 04/05/2009 - 06/05/2009, Dresden, Alemania. ISBN 9780819476371.

Descripción

Título: Approach to an FPGA embedded, autonomous object recognition system: run-time learning and adaptation
Autor/es:
  • Salvador Perea, Rubén
  • Terleira, Carlos
  • Moreno González, Félix Antonio
  • Riesgo Alcaide, Teresa
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: VLSI Circuits and Systems IV
Fechas del Evento: 04/05/2009 - 06/05/2009
Lugar del Evento: Dresden, Alemania
Título del Libro: Proceedings of VLSI Circuits and Systems IV
Fecha: Mayo 2009
ISBN: 9780819476371
Volumen: 7363
Materias:
Palabras Clave Informales: hardware embedded intelligence, FPGA embedded system, neural networks, pattern recognition, autonomous system
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, widely used in pattern recognition, security applications and robot control have been chosen for the task of object recognition within this system. One of the main drawbacks of the implementation of traditional neural networks in reconfigurable hardware is the huge resource consuming demand. This is due not only to their intrinsic parallelism, but also to the traditional big networks designed. However, modern FPGA architectures are perfectly suited for this kind of massive parallel computational needs. Therefore, our proposal is the implementation of Tiny Neural Networks, TNN -self-coined term-, in reconfigurable architectures. One of most important features of TNNs is their learning ability. Therefore, what we show here is the attempt to rise the autonomy features of the system, triggering a new learning phase, at run-time, when necessary. In this way, autonomous adaptation of the system is achieved. The system performs shape identification by the interpretation of object singularities. This is achieved by interconnecting several specialized TNN that work cooperatively. In order to validate the research, the system has been implemented and configured as a perceptron-like TNN with backpropagation learning and applied to the recognition of shapes. Simulation results show that this architecture has significant performance benefits

Más información

ID de Registro: 5619
Identificador DC: http://oa.upm.es/5619/
Identificador OAI: oai:oa.upm.es:5619
URL Oficial: http://spie.org/x648.html?product_id=821687
Depositado por: Memoria Investigacion
Depositado el: 10 Ene 2011 09:34
Ultima Modificación: 23 Feb 2017 17:45
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