针对FPGA部署优化注意力机制，让YOLO在低功耗下实现高速检测。
No Attention, No Problem: DPU-Aware Attention Approximation in Modern YOLO on FPGA
- 改造注意力模块适配DPU指令集，用1×1卷积替代分拆操作。
- YOLOv26n在标准/定向检测中分别达34.05/29.55 FPS，功耗降3倍。
- 适合边缘端实时目标检测，尤其关注能效与硬件兼容性。
基于边缘AI加速的实时目标检测近期取得进展。边缘设备上的目标检测需兼顾精度、速度与能效。本文提出一种面向AMD FPGA上部署的注意力型YOLO变体的定制化深度学习处理器单元（DPU）感知架构。具体评估并基准测试了YOLOv26和YOLOv11两款现代注意力型YOLO模型，在Xilinx ZCU104平台上的标准与定向检测任务。通过替换不支持的激活函数、以1×1卷积替代分裂操作，并以兼容DPU的方式近似空间注意力机制。所有模型在包括COCO、Pascal VOC、KITTI、DOTA、DIOR-R及自研人体存在数据集在内的六个基准数据集上训练与评估，并在全部八个DPU配置（B512至B4096）下对比mAP、FPS、延迟、功耗与资源利用率。值得注意的是，YOLOv26n与YOLOv26n-OBB在标准与定向检测中分别实现34.05与29.55 FPS的最高端到端吞吐量，量化导致平均精度下降5%，但功耗相比现有最优方案降低约3倍。
原文摘要 · Abstract (English)
Edge-based Artificial Intelligence (AI) acceleration has recently improved progress in real-time object detection. Object detection on edge devices requires a balance between accuracy, speed, and power efficiency. This paper proposes a customized Deep Learning Processor Unit (DPU)-aware architecture for attention-based YOLO variants deployed on AMD FPGAs. Specifically, we evaluate and benchmark YOLOv26 and YOLOv11, two modern attention-based YOLO variants, on the Xilinx ZCU104 across both standard and oriented object detection tasks. We replace unsupported activation functions, substitute split operations with 1x1 convolutions, and approximate the spatial attention mechanism in a DPU-compatible way. All models are then trained and evaluated across six benchmark datasets such as COCO, Pascal VOC, KITTI, DOTA, DIOR-R, and an in-house human presence dataset, and benchmarked across all eight DPU configurations (B512 to B4096) in terms of mAP, FPS, latency, power, and resource utilization. Notably, YOLOv26n and YOLOv26n-OBB deliver the highest end-to-end throughput at 34.05 and 29.55 FPS for standard and oriented detection, respectively, with an average of 5% absolute reduction in accuracy due to quantization while achieving up to approximately 3x lower power consumption compared with the state of the art.
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