Process and Runtime Variation Robustness for Spintronic-Based Neuromorphic Fabric

Abstract

Neural Networks (NN) can be efficiently accelerated using emerging resistive non-volatile memories (eNVM), such as Spin Transfer Torque Magnetic RAM(STT-MRAM). However, process variations and runtime temperature fluctuations can lead to miss-quantizing the sensed state and in turn, degradation of inference accuracy. We propose a design-time reference current generation method to improve the robustness of the implemented NN under different thermal and process variation scenarios with no additional runtime hardware overhead compared to existing solutions.