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

TL;DR

This paper presents a novel memristor crossbar array implementation of fast ICA for blind image source separation, demonstrating improved image contrast and similarity over traditional software methods.

Contribution

It introduces a memristor-based hardware architecture for fast ICA, enabling efficient and effective blind source separation in images.

Findings

Achieved 67.27% improvement in structural similarity index.

Demonstrated effective separation of image sources.

Enhanced image contrast using memristor crossbar array implementation.

Abstract

Independent component analysis is an unsupervised learning approach for computing the independent components (ICs) from the multivariate signals or data matrix. The ICs are evaluated based on the multiplication of the weight matrix with the multivariate data matrix. This study proposes a novel memristor crossbar array for the implementation of both ACY ICA and Fast ICA for blind source separation. The data input was applied in the form of pulse width modulated voltages to the crossbar array and the weight of the implemented neural network is stored in the memristor. The output charges from the memristor columns are used to calculate the weight update, which is executed through the voltages kept higher than the memristor Set/Reset voltages. In order to demonstrate its potential application, the proposed memristor crossbar arrays based fast ICA architecture is employed for image source separation problem. The experimental results demonstrate that the proposed approach is very effective to separate image sources, and also the contrast of the images are improved with an improvement factor in terms of percentage of structural similarity as 67.27% when compared with the software-based implementation of conventional ACY ICA and Fast ICA algorithms.