Low-Energy Implementation of Feed-Forward Neural Network With Back-Propagation Algorithm Using a Spin-Orbit Torque Driven Skyrmionic Device

Abstract

In this paper, we have proposed a spin-orbit torque driven skyrmionic device that acts as a synaptic element in a hardware artificial neural network (ANN) and compared its performance with similar domain wall-based device in the presence of defects in the material that can pin the domain wall or the skyrmion. Next, we have simulated a feedforward ANN with either a domain wall device or a skyrmionic device as the synaptic element and solved a standard digit recognition problem with it using the back-propagation algorithm to show that a skyrmionic synapse-based ANN consumes two orders of magnitude lower energy than a domain wall synapse-based ANN.