Analog in-memory computing (AIMC) accelerators can deliver significant energy efficiency over conventional architectures, but their accuracy is limited by device and circuit-level non-idealities. While prior work has characterized the effects of these non-idealities at model or layer granularity, their impact on individual samples remains largely unexplored. In this work, we show that an input’s vulnerability to such non-idealities can be strongly predicted by its loss curvature, a metric capturing how sharply the loss changes under small input perturbations. Across multiple models, datasets, and non-idealities, our experiments reveal a strong positive correlation between input loss curvature and analog non-ideality-induced failures, with failure rates increasing substantially for high-curvature samples. These findings uncover a previously overlooked, sample-level dimension of hardware robustness and suggest new opportunities for input-aware strategies for addressing non-idealities.

Label corruption, where training samples are mislabeled due to non-expert annotation or adversarial attacks, significantly degrades model performance. Acquiring large, perfectly labeled datasets is costly, and retraining models from scratch is computationally expensive. To address this, we introduce Scaled Activation Projection (SAP), a novel SVD (Singular Value Decomposition)-based corrective machine unlearning algorithm. SAP mitigates label noise by identifying a small subset of trusted samples using cross-entropy loss and projecting model weights onto a clean activation space estimated using SVD on these trusted samples. This process suppresses the noise introduced in activations due to the mislabeled samples. In our experiments, we demonstrate SAP’s effectiveness on synthetic noise with different settings and real-world label noise. SAP applied to the CIFAR dataset with 25% synthetic corruption show upto 6% generalization improvements. Additionally, SAP can improve the generalization over noise robust training approaches on CIFAR dataset by ∼ 3.2% on average. Further, we observe generalization improvements of 2.31% for a Vision Transformer model trained on naturally corrupted Clothing1M.