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
