Full CMOS implementation of bidirectional associative memory neural network with analog memristive synapse

Abstract

Memristor is a computational and area efficient substitute for resistive synapse in neural networks as it provides tunable and non-volatile storage of synaptic weights. Full CMOS circuit realisation of a 6 × 6 Bidirectional Associative Memory (BAM) neural network with CMOS memristor synapse is implemented in this paper. To show the BAM neural network’s ability to recall its pattern, we have taken the training set of three Tetris pattern pairs, and corresponding synapse weights are calculated using MATLAB. We have integrated the proposed tunable CMOS memristor emulator in the crossbar for storing the synaptic weights. The CMOS circuit implementation of the BAM neural network is validated with the simulation results in 0.18µm CMOS technology.