Analog Neural Circuit and Hardware Design of Deep Learning Model
Masashi Kawaguchi, Naohiro Ishii, Masayoshi Umeno · Procedia Computer Science · 2015
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 a network. In this study, we used analog electronic multiple and sample hold circuits. The connecting weights describe the input voltage. 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 includes only one input and one output network. We improved the number of unit and network layers. Moreover, we suggest the possibility of the realization the hardware implementation of the deep learning model.