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

Moreno González, Félix Antonio and Alarcón, Jaime and Salvador Perea, Rubén and 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.

Description

Title: Reconfigurable hardware architecture of a shape recognition system based on specialized tiny neural networks with online training.
Author/s:
  • Moreno González, Félix Antonio
  • Alarcón, Jaime
  • Salvador Perea, Rubén
  • Riesgo Alcaide, Teresa
Item Type: Article
Título de Revista/Publicación: IEEE Transactions on Industrial Electronics.
Date: August 2009
ISSN: 0278-0046
Volume: 56
Subjects:
Freetext Keywords: Neural network hardware implementation, run-time learning, recognition.
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 (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.

More information

Item ID: 5084
DC Identifier: http://oa.upm.es/5084/
OAI Identifier: oai:oa.upm.es:5084
DOI: 10.1109/TIE.2009.2022076
Official URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04926188
Deposited by: Memoria Investigacion
Deposited on: 25 Nov 2010 11:35
Last Modified: 23 Feb 2017 17:32
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