AI RESEARCH
Spatiotemporal-Aware Bit-Flip Injection on DNN-based Advanced Driver Assistance Systems
arXiv CS.LG
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ArXi:2604.03753v1 Announce Type: cross Modern advanced driver assistance systems (ADAS) rely on deep neural networks (DNNs) for perception and planning. Since DNNs' parameters reside in DRAM during inference, bit flips caused by cosmic radiation or low-voltage operation may corrupt DNN computations, distort driving decisions, and lead to real-world incidents. This paper presents a SpatioTemporal-Aware Fault Injection (STAFI) framework to locate critical fault sites in DNNs for ADAS efficiently.