Variation Aware Training of Hybrid Precision Neural Networks with 28nm HKMG FeFET Based Synaptic Core
This work addresses hardware-aware training for neuromorphic computing, but it appears incremental as it builds on existing hybrid precision and FeFET methods.
The authors tackled the challenge of training neural networks with hybrid precision using FeFET-based synaptic cores, achieving up to 95% inference accuracy despite device variations.
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.