Multilayer Neural Network with Synapse Based on Two Successive Memristors

Abstract

Synapse based on two successive memristors builds the synaptic weights of the artificial neural network for training three-bit parity problem and five-character recognition. The proposed memristor synapse circuit creates positive weights in the range [0;1], and maps it to range [-1;1] to program both the positive and negative weights. The proposed scheme achieves the same accuracy rate as the conventional bridge synapse schemes which consist of four memristors. However, proposed synapse circuit decreases 50% the number of memristors and 76.88% power consumption compared to the conventional bridge memristor synapse.