Towards Robust RRAM-Based Vision Transformer Models with Noise-Aware Knowledge Distillation

Resistive random-access memory (RRAM)-based compute-in-memory (CIM) systems show promise in accelerating Transformer-based vision models but face challenges from inherent device non-idealities. In this work, we systematically investigate the vulnerability of Transformer-based vision models to RRAM-induced perturbations. Our analysis reveals that earlier Transformer layers are more vulnerable than later ones, and feed-forward networks (FFNs) are more susceptible to noise than multi-head self-attention (MHSA). Based on these observations, we propose a noise-aware knowledge distillation framework that enhances model robustness by aligning both intermediate features and final outputs between weight-perturbed and noise-free models. Experimental results demonstrate that our method improves accuracy by up to 1.54% and 1.49% on ViT and DeiT models under various noise conditions compared to their vanilla counterparts.