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Proceedings Paper

Pulse-coupled neural network implementation in FPGA
Author(s): Joakim T. A. Waldemark; Thomas Lindblad; Clark S. Lindsey; Karina E. Waldemark; Johnny Oberg; Mikael Millberg
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Paper Abstract

Pulse Coupled Neural Networks (PCNN) are biologically inspired neural networks, mainly based on studies of the visual cortex of small mammals. The PCNN is very well suited as a pre- processor for image processing, particularly in connection with object isolation, edge detection and segmentation. Several implementations of PCNN on von Neumann computers, as well as on special parallel processing hardware devices (e.g. SIMD), exist. However, these implementations are not as flexible as required for many applications. Here we present an implementation in Field Programmable Gate Arrays (FPGA) together with a performance analysis. The FPGA hardware implementation may be considered a platform for further, extended implementations and easily expanded into various applications. The latter may include advanced on-line image analysis with close to real-time performance.

Paper Details

Date Published: 25 March 1998
PDF: 11 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304829
Show Author Affiliations
Joakim T. A. Waldemark, Royal Institute of Technology (Sweden)
Thomas Lindblad, Royal Institute of Technology (Sweden)
Clark S. Lindsey, Royal Institute of Technology (Sweden)
Karina E. Waldemark, Royal Institute of Technology (Sweden)
Johnny Oberg, Royal Institute of Technology (Sweden)
Mikael Millberg, Royal Institute of Technology (Sweden)


Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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