Improved winner-take-all circuit for neural network based on frequency-modulated signals

Abstract

This paper proposes a new winner-take-all (WTA) circuit for WTA neural network (WTANN) that is based on frequency modulated signals. WTA finds winner neuron that has the nearest internal weight vector to input vector, and reliable and efficient frequency comparator is required for the implementation of the WTA circuit. This paper proposes a cycle slip detector to estimate frequency difference of the signals. To evaluate the performance of the proposed WTA, VHDL simulation was conducted. Results revealed that accuracy in WTA operation of the proposed method is much better than the previously proposed WTA circuit.