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

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

Description

Title: Approach to an FPGA embedded, autonomous object recognition system: run-time learning and adaptation
Author/s:
  • Salvador Perea, Rubén
  • Terleira, Carlos
  • Moreno González, Félix Antonio
  • Riesgo Alcaide, Teresa
Item Type: Presentation at Congress or Conference (Article)
Event Title: VLSI Circuits and Systems IV
Event Dates: 04/05/2009 - 06/05/2009
Event Location: Dresden, Alemania
Title of Book: Proceedings of VLSI Circuits and Systems IV
Date: May 2009
ISBN: 9780819476371
Volume: 7363
Subjects:
Freetext Keywords: hardware embedded intelligence, FPGA embedded system, neural networks, pattern recognition, autonomous system
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, 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

More information

Item ID: 5619
DC Identifier: http://oa.upm.es/5619/
OAI Identifier: oai:oa.upm.es:5619
Official URL: http://spie.org/x648.html?product_id=821687
Deposited by: Memoria Investigacion
Deposited on: 10 Jan 2011 09:34
Last Modified: 23 Feb 2017 17:45
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