BARE-AI: Bit-Flip Attack Resilience in AI Hardware through Built-in Performance Monitors
[Kryptografia i bezpieczeństwo] Autorzy: Habibur Rahaman, Swastik Bhattacharya, Sanjay Das i 2 więcej
Deep Neural Networks (DNNs) are integral to many safety critical systems, yet they remain highly vulnerable to bit-flip attacks (BFAs), where a few memory level perturbations can drastically degrade accuracy. Existing defenses incur significant hardware overhead, depend on retraining, or fail against targeted flips. We propose BARE-AI, a runtime framework that detects, localizes, and mitigates BFAs during inference. BARE-AI introduces AI Performance Counters (APCs), lightweight hardware monitors in the accelerator datapath that capture per-layer activation statistics such as sparsity, entropy, kurtosis, and spectral shift. These are analyzed by the Predictive Unit for Layer Security Evaluation (PULSE), a compact detector trained offline as an ensemble of classifiers and realized on-chip as a small neural engine. For explainability and recovery, BARE-AI introduces an Activation Shift Ind…
- Kategoria arxiv
- cs.CR
- Kategoria
- Kryptografia i bezpieczeństwo