Analog Neural Circuit by AC Operation and the Design of Deep Learning Model

Abstract

In the neural network field, many application models have been proposed. Previous analog neural network models were composed of the operational amplifier and fixed resistance. It is difficult to change the connecting weight of network. In this study, we used analog electronic AC circuits. The connecting weights describe the voltage and input signal describe the frequency. It is easy to change the connection coefficient. This model works only on analog electronic circuits. It can finish the learning process in a very short time and this model will enable more flexible learning. However, the structure of this model is only one input and one output network. We improved the number of unit and network layer. Moreover, we suggest the possibility of realization about the hardware implementation of the deep learning model.