Method and system for a temperature-resilient neural network training model

A method for elevating a model robustness to a temperature-induced retention failure of a neural network, the method comprising:

• modeling RRAM non-ideality based on real RRAM-chip measurements using a resistive nonvolatile in-memory-computing chip;
• training a deep neural network using a progressive knowledge distillation algorithm to distill robustness from a teacher model to a student model, the progressive knowledge distillation algorithm comprising:
• injecting low temperature noises to the student model using a clean model as the teacher model;
• injecting, now using the student model as the teacher model, high temperature noises to an inherited student model;
• and
• training the deep neural network model, while the model remains fixed, using a batch normalization adaptation algorithm, wherein the batch normalization adaptation algorithm includes training a plurality of batch normalization parameters with respect to a plurality of thermal variations.