LAUSR

This paper proposes a unique hardware architecture for a self-organizing map (SOM) that mimics the biological brain by using pulse mode operation. In the proposed SOM, vector elements are given as in the form of frequency modulated signals, and digital frequency-locked loops (DFLLs) in neurons handle the computations of the vector elements. The SOM is trained by unsupervised learning, where the winner neuron that has the nearest weight vector is found first. In the proposed SOM, the winner neuron is found by counting cycle slips between the signals that carry input and weight vectors. After the winner neuron is found, weight vectors selected by a neighborhood function are updated toward the input vector. Triangular neighborhood function that is implemented by using an attenuating enable signal for the DFLLs, is employed. To evaluate the proposed SOM and its building components, VHDL simulations and experiments using an FPGA were conducted. Compared to the previous work, the operation speed and learning capability were significantly improved. Novelty of the proposed architecture is it uniquely uses a pulse-based operation that mimics the biological brain, and it was verified that unsupervised learning can be realized with neurons communicating with each other using frequency modulated pulse signals.