  This work proposes a hybrid-precision neural network training framework with
an eNVM based computational memory unit executing the weighted sum operation
and another SRAM unit, which stores the error in weight update during back
propagation and the required number of pulses to update the weights in the
hardware. The hybrid training algorithm for MLP based neural network with 28 nm
ferroelectric FET (FeFET) as synaptic devices achieves inference accuracy up to
95% in presence of device and cycle variations. The architecture is primarily
evaluated using behavioral or macro-model of FeFET devices with experimentally
calibrated device variations and we have achieved accuracies compared to
floating-point implementations.
