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BARE-AI, a runtime framework that detects, localizes, and mitigates BFAs during inference, and introduces AI Performance Counters, lightweight hardware monitors in the accelerator datapath that capture per-layer activation statistics such as sparsity, entropy, kurtosis, and spectral shift.
Large language models (LLMs) incur substantial storage, memory-bandwidth and energy costs, motivating compact weight representations. Existing seed-based compression methods reconstruct weights from compact pseudo-random representations but do not explicitly account for the non-uniform sensitivity of model weights. We...
A multi-strategy vulnerability analysis methodology is presented, combining simulation-based verification, formal verification, lint analysis, Large Language Model-assisted bug detection, and coverage-guided hybrid fuzzing to derive practical lessons for pre-silicon security verification.
Patients performing knee rehabilitation using physiotherapy outside clinical settings have no reliable means of verifying whether their movements meet therapeutic standards. This paper presents a pasteable, skin-mounted sensing system that combines lightweight inertial measurement units (IMUs) with machine learning to...
This survey presents a comprehensive overview of state-of-the-art hardware fuzzing research, examining how existing hardware fuzzing approaches differ in their fundamental approaches to test generation, simulation infrastructure, mutation strategy, coverage feedback, and bug detection.
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