Implementation of fast ICA using memristor crossbar arrays for blind image source separations

Abstract

Independent component analysis (ICA) is an unsupervised learning approach for computing the independentcomponents (ICs) from the multivariate signals or data matrix. The ICs are evaluated based on the multiplication of the weightmatrix with the multivariate data matrix. This study proposes a novel Pt/Cu:ZnO/Nb:STO memristor crossbar array for theimplementation of both ACY ICA and Fast ICA for blind source separation. The data input was applied in the form of pulse widthmodulated voltages to the crossbar array and the weight of the implemented neural network is stored in the memristor. Theoutput charges from the memristor columns are used to calculate the weight update, which is executed through the voltageskept higher than the memristor Set/Reset voltages (±1.30 V). In order to demonstrate its potential application, the proposedmemristor crossbar arrays based fast ICA architecture is employed for image source separation problem. The experimentalresults demonstrate that the proposed approach is very effective to separate image sources, and also the contrast of the imagesare improved with an improvement factor in terms of percentage of structural similarity as 67.27% when compared with thesoftware-based implementation of conventional ACY ICA and Fast ICA algorithms.