Stochastic Spintronic Neuron with Application to Image Binarization

Abstract

The hardware implementation of neural network has always been of interest to the researchers as it can significantly increase the efficiency and application of neural networks due to the distributed nature of Artificial Neural Networks (ANNs) in both memory and computation. Direct implementation of ANNs also offer large gains when scaling the network sizes. Stochastic neurons are among the most significant aspects of machine learning algorithms and are very important in different neural networks. In this paper, a hardware model for the stochastic neuron based on the magnetic tunnel junction (MTJ) in subcritical current switching regime is proposed. Functional evaluation of the proposed model demonstrates that the behavior of the proposed model is comparable to the mathematical description of the stochastic neuron, and it has a negligible error in comparison with the theoretical model. The simulation results of image binarization over 10,000 images indicate that the proposed hardware model has only 0.25% pack signal to noise ratio (PSNR) and 0.02% structural similarity (SSIM) variation compared to its software-based counterpart.