XiYOLO: Energy-Aware Architecture Search for Object Detection from Sparse Hardware Measurements

Object detection on heterogeneous edge devices must satisfy strict energy, latency, and memory constraints while still providing reliable perception for downstream autonomy. Existing energy-aware NAS methods often target limited deployment settings, while real energy remains difficult to optimize because it is highly device-dependent and costly to measure. We address these challenges with an energy-adaptive framework that combines an energy-aware XiResOFA search space, a two-stage energy estimator, and iterative multi-objective architecture search. The search yields an accuracy–energy Pareto frontier from which an operating point can be selected according to mission-specific energy requirements. We select a balanced operating point from this frontier as the reference architecture and apply post-search scaling to construct the XiYOLO family. Experiments on PascalVOC, COCO, and real-device deployment show that XiYOLO achieves a stronger energy–accuracy tradeoff than YOLO baselines. On PascalVOC, the medium XiYOLO model reaches 86.15 mAP50 while reducing energy relative to YOLOv12m by 20.6% on GPU and 35.9% on NPU. On COCO, XiYOLO reduces energy relative to YOLOv12 by up to 53.7% on GPU and 51.6% on NPU at the small scale. With only 20 target-device measurements, the proposed two-stage estimator recovers 88.6% and 93.0% of dense-reference search hypervolume on COCO and PascalVOC, respectively, corresponding to 38.1% and 27.6% improvements over joint adaptation.