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This work proposes a hybrid-precision neural network training framework withan eNVM based computational memory unit executing the weighted sum operationand another SRAM unit, which stores the error in weight update during backpropagation and the required number of pulses to update the weights in thehardware. The hybrid training algorithm for MLP based neural network with 28 nmferroelectric FET (FeFET) as synaptic devices achieves inference accuracy up to95% in presence of device and cycle variations. The architecture is primarilyevaluated using behavioral or macro-model of FeFET devices with experimentallycalibrated device variations and we have achieved accuracies compared tofloating-point implementations.

Variation Aware Training of Hybrid Precision Neural Networks with 28nm HKMG FeFET Based Synaptic Core