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：空中搜索与救援任务要求在不确定且快速变化的环境下实现快速可靠的遇险人员检测。确定性深度学习模型可能产生过于自信的误报，迫使无人机系统执行代价高昂的验证动作，从而降低搜索覆盖率并延误救援时间。贝叶斯神经网络提供了不确定性感知检测，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，其中集成了一种基于无写入中心极限定理的高斯随机数发生器，内置于存内计算宏中。通过从随机选定的一组最小尺寸、一次性编程的FeFET子集中对电流求和，所提出的架构在推理过程中消除了能量密集且受耐久性限制的写入操作，同时保持了可扩展的高斯采样。该CLT-GRNG每采样消耗640 aJ能量，较先前的BNN加速器实现了560倍的能效提升，而CIM模块实现了185 TOPS/W/mm²的性能。在基于空中搜索与救援检测的评估中，贝叶斯模型改善了环境退化条件下的不确定性校准与鲁棒性，降低了风险，并允许在代价高昂的验证之前过滤低置信度的检测结果。这些结果表明，该技术为自主搜索与救援系统提供了一种能效高且具备不确定性感知能力的边缘AI引擎。