Pulse Density Recurrent Neural Network Systems with Learning Capability Using FPGA

In this paper, we present FPGA recurrent neural network systems with learning capability using the simultaneous perturbation learning rule. In the neural network systems, outputs and internal values are represented by pulse train. That is, analog recurrent neural networks with pulse frequency representation are considered. The pulse density representation and the simultaneous perturbation enable the systems with learning capability to easily implement as a hardware system. As typical examples of the recurrent neural networks, Hopfield neural network and the bidirectional associative memory are considered. Details of the systems and the circuit design are described. Analog and digital examples for these Hopfield neural network and the bidirectional associative memory are also shown to confirm a viability of the system configuration and the learning capability. Key-Words: Hardware implementation, Pulse density, Hopfield neural network, Bidirectional associative memory, Learning, Simultaneous perturbation, FPGA