The Application of FPGA Based Real-Time Processing ESN in Pattern Recognition and Waveform Generation

Abstract

In this paper, a real-time hardware-based Field Programmable Gate Array (FPGA) architecture Echo state network (ESN) of recurrent neural network (RNN), which real-timely gets the output weights of Reservoir Computing (RC) in FPGA, was used for pattern recognition and waveform generation. It is designed in strict accordance with the RC theory. The four parts of RC, which is input layer, reservoir layer, output layer, and weight training block, were all built in FPGA. The training of the RC was finished in real-time. We have verified its performance through implemented and verification in Altera FPGA. Experimental results showed that the real-time hardware RC can remember the ratio and recognize different period after it was trained as it was trained.