Neuromorphic Hardware Using Simplified Elements and Thin-Film Semiconductor Devices as Synapse Elements - Simulation of Hopfield and Cellular Neural Network -

Abstract

Neuromorphic hardware using simplified elements and thin-film semiconductor devices as synapse elements is proposed. It is assumed that amorphous metal-oxide semiconductor devices are used for the synapse elements, and the characteristic degradation is utilized for the learning rule named modified Hebbian learning. First, we explain an architecture and operation of a Hopfield neural network. Next, we model the electrical characteristic of the thin-film semiconductor devices and simulate the letter recognition by the neural network. Particularly in this presentation, we show a degradation map. On the other hand, we also explain an architecture and operation of a cellular neural network, model the thin-film semiconductor devices, and simulate the letter recognition. Particularly in this presentation, we evaluate connection schemes. It is found that the cellular neural network has higher performance when it has diagonal connections. Moreover, we compare the Hopfield and cellular neural networks. It is found that the Hopfield neural network has higher performance, although the cellular neural network has a simple structure.