ReAnchor: Input-Conditioned Post-Backbone Representation Recovery under Soft Errors

Transient soft errors in hardware can corrupt DNN weights or intermediate activations during inference and propagate to incorrect predictions. Many algorithm-level mitigation methods require fault-aware retraining or modifications to the backbone, limiting their use with already-trained models. Range-based mitigation is less intrusive, yet residual errors at the backbone–head interface may remain within calibrated ranges or persist after range projection. This motivates recovering task-relevant representations directly at the post-backbone interface. We propose ReAnchor for this setting. It first applies a fixed range projection to stabilize the terminal backbone feature and then combines the projected feature with sample-aligned multi-scale context extracted from the current input to recover a task-relevant representation for the original task head. Only the ReAnchor module is post-trained; the backbone and task head remain fixed, and inference requires no knowledge of the fault location, corrupted bit index, or fault rate. Across three common datasets and three different model architectures, ReAnchor largely preserves clean accuracy while its advantage becomes more apparent as the injected fault rate increases. At a fault rate of $10^{-5}$, ReAnchor achieves the highest mean top-1 accuracy in all nine dataset–backbone settings. Compared with the best competing method in each setting, including the unmitigated baseline and the four range-based methods, the average improvement is 31.4 percentage points.