Abstract
This paper proposes very unique hardware architecture for self-organizing map (SOM) that is based on frequency modulated (FM) signal. The proposed SOM architecture consists of neurons embedded with winner search and neighborhood function circuits, all of which employ pulse operation. The neuron uses digital frequency-locked loop (DFLL) as its computing element. In terms of modeling the brain in signal level, this approach in the hardware SOM architecture is very significant. The proposed SOM was implemented on FPGA, and its on-chip learning performance was tested by experiments. Preliminary experimental results showed that the proposed hardware SOM was successfully trained to various input vectors, showing topology-preserving nature.
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