A Spin-Orbit Torque based Cellular Neural Network (CNN) Architecture

Yu Bai, X. Sharon Hu, Ronald F. DeMara, Mingjie Lin

Abstract: In this paper, we propose a differential Spin Hall Effect(SHE) assisted domain wall synapse, which can generate either positive or negative synaptic weighting values without the significant cost of multiple power supply voltages, supply rails, or computationally-intensive digital hardware. The architecture of the proposed synapse utilizes reading currents flowing through two oppositely-oriented devices as weighted by device conductance. The conductance is used to encode synaptic weight and programmed by domain wall position through writing current. The ability to set the current as positively or negatively weighted results in highly-configurable functionality within a compact synapse design. The synapses are used with a soft-limiting nonlinear neuron to employ the relationship between positions and input current magnitude. We show through micro-magnetic simulation how the non-volatile physical characteristic of the domain wall calibrated synapse is used to implement a numerical integration function to realize a Cellular Neural Network(CNN). The performance of the proposed CNN design for isolated letter denoising at 0ns to 4ns demonstrates noise filtering functionality with total energy consumption during sensing of 24fJ. This compares favorably to existing spin CNN cell designs to provide a promising design approach for intrinsic neural computation.