Aerial search and rescue missions require fast and reliable victim detection under uncertain and rapidly changing environments. Deterministic deep learning models can produce overconfident false positives, forcing unmanned aircraft systems to perform costly verification maneuvers that reduce search coverage and increase rescue delay. Bayesian neural networks provide uncertainty-aware detection, but their sampling overhead is challenging for battery-constrained edge platforms. This work presents a FeFET-based Bayesian inference engine with a write-free central limit theorem Gaussian random number generator embedded in a compute-in-memory macro. By summing currents from a randomly selected subset of minimum-sized, programmed-once FeFETs, the proposed architecture eliminates energy- and endurance-intensive write operations during inference while maintaining scalable Gaussian sampling. The CLT-GRNG consumes 640 aJ per sample, providing a 560x energy-efficiency improvement over prior BNN accelerators, while the CIM tile achieves 185 TOPS/W/mm2. Evaluated on aerial search and rescue detection, the Bayesian model improves uncertainty calibration and robustness under environmental corruption, reducing risk and enabling low-confidence detections to be filtered before costly verification. These results demonstrate an energy-efficient and uncertainty-aware edge AI engine for autonomous search and rescue systems.

翻译：空中搜救任务要求在不确定且快速变化的环境下快速可靠地检测遇难者。确定性深度学习模型可能产生过度自信的误报，迫使无人机系统执行代价高昂的验证机动，从而降低搜索覆盖率并增加救援延误。贝叶斯神经网络提供了不确定性感知的检测能力，但其采样开销对电池受限的边缘平台构成挑战。本文提出了一种基于FeFET的贝叶斯推理引擎，该引擎采用嵌入存算一体宏单元的无写入中心极限定理高斯随机数生成器。通过从随机选择的、编程一次的最小尺寸FeFET子集中汇总电流，所提出架构消除了推理过程中能耗高且耐久性差的写入操作，同时保持了可扩展的高斯采样能力。该CLT-GRNG每次采样仅消耗640 aJ，相较于先前的BNN加速器实现了560倍的能效提升，而CIM瓦片达到了185 TOPS/W/mm²的能效密度。在航空搜救探测任务上的评估表明，该贝叶斯模型在环境干扰下改善了不确定性校准和鲁棒性，降低了风险，并允许在代价高昂的验证前过滤低置信度检测结果。这些成果为自主搜救系统提供了一种高能效且具备不确定性感知能力的边缘AI引擎。