On Robustness of Spin-Orbit-Torque Based Stochastic Sigmoid Neurons for Spiking Neural Networks

Nano-scale neuro-mimetic devices have recently gained wide research interest in the quest to enable brain-like energy-efficiency with cognitive computing abilities. Traditionally, neuromorphic devices have exploited deterministic nano-scale devices for emulating the intrinsic neuronal and synaptic behavior. However, of particular interest are stochastic neuromorphic devices owing to - 1) availability of nano-scale devices that are inherently stochastic based on intrinsic device physics 2) various neuroscience experiments have demonstrated that cortical neurons are stochastic in nature. In this paper, we focus on spin orbit torque based Magnetic Tunnel Junction (SOT-MTJ) that exhibit stochastic sigmoid behavior with respect to the switching process. We first discuss the modeling framework that was used to study the effect of dimensional variations in SOT-MTJs and the resulting changes in the stochastic sigmoid behavior. Our model is based on the well-known stochastic-Landau-Lifshitz-Gilbert-Slonczewski equation under mono-domain approximation. Subsequently, we abstract the sigmoid characteristic of the device into a behavioral model and study the effect of variations in sigmoid characteristics on a deep binary network. Our results show that the variations in the sigmoidal neuron behavior results in a minimal loss in accuracy (for CIFAR 10 dataset). Additionally, the degradation in accuracy monotonically increases with increase in induced variations. This highlights the robustness of stochastic neural networks based on SOT-MTJs in presence of dimensional variations.