Neuromorphic architecture based on orthogonal spin current injected MTJs

Neuromorphic computing is inspired by the human brain and can typically solve certain computational problems with high power efficiency and lesser delay. Spintronics devices can potentially provide a better hardware platform for energy-efficient neuromorphic computing than CMOS counterparts. In this work, we propose a circuit based on orthogonal spin current injected magnetic tunnel junctions (MTJs) to simultaneously perform various functions of convolutional neural network (CNN). We have developed a computational platform that incorporates HSPICE and the non-equilibrium Green's function (NEGF) approach to evaluate the performance of the proposed circuit. We show linearized switching of the MTJ using the orthogonally injected spin current. Using the linear switching region of the MTJ, we show the proposed circuit performs the simultaneous CNN functions such as rectified linear unit (ReLU) and the local max-pooling functions. Our simulations also demonstrate the robustness of the proposed circuit against thermal noise.