Pulse-coupled neural network implementation in FPGA

Joakim T. A. Waldemark; Thomas Lindblad; Clark S. Lindsey; Karina E. Waldemark; Johnny Oberg; Mikael Millberg

doi:10.1117/12.304829

25 March 1998 Pulse-coupled neural network implementation in FPGA

Joakim T. A. Waldemark, Thomas Lindblad, Clark S. Lindsey, Karina E. Waldemark, Johnny Oberg, Mikael Millberg

Proceedings Volume 3390, Applications and Science of Computational Intelligence; (1998) https://doi.org/10.1117/12.304829
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

Citation Download Citation

Joakim T. A. Waldemark, Thomas Lindblad, Clark S. Lindsey, Karina E. Waldemark, Johnny Oberg, and Mikael Millberg "Pulse-coupled neural network implementation in FPGA", Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); https://doi.org/10.1117/12.304829

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