Mixed-mode Magnetic Tunnel Junction-based Deep Belief Network

We present a mixed-mode magneto tunneling junction (m-MTJ)-based Deep Belief Network (DBN). DBNs are unsupervised learning models, suitable for recognition and clustering. m-MTJ is a three-terminal magnetic device with probabilistic free layer switching controlled by the simultaneous actions of voltage-controlled magnetic anisotropy and spin-transfer torque. While DBNs achieve high prediction accuracy even with highly imprecise single-bit weights, the key complexity lies in their activation functions which are stochastic. Using an m-MTJ, we present a novel low area/power DBN neuron with stochastic activation function. We discuss an in-memory computing architecture that allows forward and backward flow of learning dynamics and online learning. Our design achieves ~88.80% accuracy for digit recognition in MNIST even under the worst case variability in nanoscaled m-MTJs.