Conductance Variation-Assisted Adversarial Attack Robustness on 40nm TaOx-based ReRAM CiM

This work investigates the conductance variation-assisted enhancement of neural network (NN) robustness against adversarial attacks, exploiting the measurements of 32K bits 40nm TaOx-based analog Resistive Random Access Memory (ReRAM) devices. Proposed Computation-in-Memory (CiM)-aware adversarial training enhances NN robustness against both adversarial attacks and conductance variations by integrating the measured variations with training, improves classification accuracy of Fast Gradient Sign Method (FGSM)-attacked CIFAR-10 on ResNet-32 by 25% and that of Projected Gradient Descent (PGD)-attacked by 22%.