低功耗无人机侦测芯片，帧与事件混合追踪提升效率
A 96pJ/Frame/Pixel and 61pJ/Event Anti-UAV System with Hybrid Object Tracking Modes
- 融合帧与事件数据，动态切换追踪模式以适应目标大小速度
- 每帧每像素仅96皮焦，每事件61皮焦，识别准确率达98.2%
- 适合对功耗敏感的便携式反无人机系统部署
我们提出一种低功耗反无人机系统，结合帧基与事件驱动的目标追踪，实现对小型快速移动无人机的可靠检测。系统通过游程编码重建二值事件帧，生成区域建议，并根据目标尺寸与速度自适应切换帧模式与事件模式。快速目标追踪单元通过自适应阈值与基于轨迹的分类提升高速目标鲁棒性。神经处理单元支持灰度块与轨迹推理，采用定制指令集和零跳过MAC架构，将冗余神经计算减少超过97%。该芯片采用40 nm CMOS工艺，面积2 mm²，工作在0.8 V下，每帧每像素功耗为96 pJ，每事件功耗为61 pJ；在50至400米距离、5至80像素/秒速度范围内，于公开无人机数据集上达到98.2%的识别准确率。结果表明，该系统在端到端能效方面达到当前反无人机系统最优水平。
原文摘要 · Abstract (English)
We present an energy-efficient anti-UAV system that integrates frame-based and event-driven object tracking to enable reliable detection of small and fast-moving drones. The system reconstructs binary event frames using run-length encoding, generates region proposals, and adaptively switches between frame mode and event mode based on object size and velocity. A Fast Object Tracking Unit improves robustness for high-speed targets through adaptive thresholding and trajectory-based classification. The neural processing unit supports both grayscale-patch and trajectory inference with a custom instruction set and a zero-skipping MAC architecture, reducing redundant neural computations by more than 97 percent. Implemented in 40 nm CMOS technology, the 2 mm^2 chip achieves 96 pJ per frame per pixel and 61 pJ per event at 0.8 V, and reaches 98.2 percent recognition accuracy on public UAV datasets across 50 to 400 m ranges and 5 to 80 pixels per second speeds. The results demonstrate state-of-the-art end-to-end energy efficiency for anti-UAV systems.
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