Reconfigurable Self-Organizing Neural Network Design and it's FPGA Implementation

Abstract

Abstract The use of Kohonen self-organizing feature maps in real time applications requires high computational performance, especially for embedded systems and hence neural network chips are essential. A digital architecture of Kohonen neural network with learning capability and on-chip adaptation and storage is proposed with the implementation of Kohonen Self-Organizing Map (SOM) neural networks on the low-cost Spartan-3 FPGAs. The architecture of this digital chip based on the idea that some assumptions for the restrictions of the algorithm can simplify the implementation. Using the Manhattan distance, a special treatment of the adaptation factor, and neighborhood functions will decrease the necessary chip area so that a high number of processing elements can be integrated on one chip. Keywords: FPGA, Weight Vectors, Manhattan distance, Learning