FPGA implementation of an image recognition system based on tiny neural networks and on-line reconfiguration

Moreno González, Félix Antonio and Alarcón Celis, Jaime and Salvador Perea, Rubén and Riesgo Alcaide, Teresa (2008). FPGA implementation of an image recognition system based on tiny neural networks and on-line reconfiguration. In: "34th Annual Conference of the IEEE Industrial Electronics Society.IECON-2008", 10/11/2008-13/11/2008, Orlando (Florida, USA). ISBN 978-14-2441-668-4. pp. 2445-2452.

Description

Title: FPGA implementation of an image recognition system based on tiny neural networks and on-line reconfiguration
Author/s:
  • Moreno González, Félix Antonio
  • Alarcón Celis, Jaime
  • Salvador Perea, Rubén
  • Riesgo Alcaide, Teresa
Item Type: Presentation at Congress or Conference (Article)
Event Title: 34th Annual Conference of the IEEE Industrial Electronics Society.IECON-2008
Event Dates: 10/11/2008-13/11/2008
Event Location: Orlando (Florida, USA)
Title of Book: Industrial Electronics, 2008. IECON 2008. 34th Annual Conference of IEEE
Date: 2008
ISBN: 978-14-2441-668-4
Subjects:
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Electrónica e Informática Industrial [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Neural networks are widely used in pattern recognition, security applications and robot control. We propose a hardware architecture system; using Tiny Neural Networks (TNN) specialized in image recognition. The generic TNN architecture allows 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 Tiny Neural Networks (TNN) is their learning ability. Weight modification and architecture reconfiguration can be carried out at run time. Our system performs shape identification by the interpretation of their singularities. This is achieved by interconnecting several specialized TNN. The results of several tests, in different conditions are reported in the paper. The system detects accurately a test shape in almost all the experiments performed. The 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 was configured as a perceptron network with backpropagation learning and applied to the recognition of shapes. Simulation results show that this architecture has significant performance benefits.

More information

Item ID: 3355
DC Identifier: http://oa.upm.es/3355/
OAI Identifier: oai:oa.upm.es:3355
Official URL: http://iecon2008.auburn.edu/index.html
Deposited by: Memoria Investigacion
Deposited on: 17 Jun 2010 08:46
Last Modified: 23 Feb 2017 17:46
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