Low Power Restricted Boltzmann Machine Using Mixed-Mode Magneto-Tunneling Junctions

Abstract

This letter discusses mixed-mode magneto tunneling junction (m-MTJ)-based restricted Boltzmann machine (RBM). RBMs are unsupervised learning models, suitable for extracting features from high-dimensional data. The m-MTJ is actuated by the simultaneous actions of voltage-controlled magnetic anisotropy and voltage-controlled spin-transfer torque, where the switching of the free-layer is probabilistic and can be controlled by the two. Using m-MTJ-based activation functions, we present a novel low area/power RBM. We discuss online learning of the presented implementation to negate process variability. For MNIST hand-written dataset, the design achieves ~96% accuracy under expected variability in various components.