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Reconfigurable Hardware Architecture of a Shape Recognition System Based on Specialized Tiny Neural Networks With Online Training.

Authors :
Moreno, Felix
Alarcón, Jaime
Salvador, Rubén
Riesgo, Teresa
Source :
IEEE Transactions on Industrial Electronics; Aug2009, Vol. 56 Issue 8, p3253-3263, 11p, 8 Diagrams, 2 Charts
Publication Year :
2009

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02780046
Volume :
56
Issue :
8
Database :
Complementary Index
Journal :
IEEE Transactions on Industrial Electronics
Publication Type :
Academic Journal
Accession number :
43657866
Full Text :
https://doi.org/10.1109/TIE.2009.2022076