FPGA Implementation of Pulse Density Hopfield Neural Network

Abstract

In this paper, we present a FPGA Hopfield Neural Network system with learning capability using the simultaneous perturbation learning rule. In the neural network, outputs and internal values are represented by pulse train. That is, analog Hopfield Neural Network with pulse frequency representation is considered. The pulse density representation and the simultaneous perturbation enable the system with learning capability to easily implement as a hardware system. Details of the design are described. Some results are also shown to confirm a viability of the system configuration and the learning capability.