In-field vulnerability analysis of hardware-accelerated computer vision applications

In this paper, we propose an FPGA-based emulation framework that can provide dynamic vulnerability analysis for hardware-accelerated computer vision applications. The framework can be integrated alongside the targeted application, to allow for run-time, in-field, dynamically adjusted vulnerability analysis in real-world conditions, taking into consideration the non-deterministic parameters of the computer vision algorithm computations. We evaluate the proposed framework in real-time using an FPGA platform, for an obstacle avoidance (OA) computer vision application and its disparity estimation kernel to study the impact of Single-Event Upsets (SEUs).

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