在低计算边缘SoC上的对象检测:可重复的基准和部署指南
Chang Kong1, Feng Li2, Xiaohu Yan1
1Undergraduate College of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen, 518055, China.
Scientific reports
|January 21, 2026
概括
在边缘AI SoC上部署深度学习对象探测器是复杂的. 推断延迟,不仅仅是计算能力,是准确性的关键,内存带宽在多任务负载下对强大的边缘AI至关重要.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 嵌入式系统 嵌入式系统
背景情况:
- 在边缘AI系统上芯片 (SoCs) 上部署深度学习对象检测器面临挑战,原因是超出理论计算等级的因素.
- 现实世界的性能受到建筑设计,内存带宽和系统级争议的影响.
研究的目的:
- 在三个Rockchip SoC上提供九个YOLO物体检测变体的全面和可重复的基准.
- 分析各种输入分辨率,计算配置和操作条件的性能.
主要方法:
- 在三个Rockchip SoC上对9个YOLO变体进行基准测试.
- 评估性能指标,包括推断延迟,检测准确度 (mAP),FLOP,参数计数和每推断能量等.
- 在不同的输入分辨率,计算设置和多任务条件下进行测试.
主要成果:
- 推断延迟与检测精度 (mAP) 相比,与FLOP或参数数量有更强的相关性.
- 延迟缩放与输入大小偏离理论预测由于内存带宽限制.
- 多核NPU调度提供了由于同步和内存瓶的边际好处.
- 记忆带宽是多任务压力下强度的主要因素.
- 在SoC中观察到显著的每推理能量差异.
结论:
- 硬件意识的模型选择和内存高效的优化对于实时边缘AI应用程序至关重要.
- 性能是由模型架构,硬件功能和系统级因素的复杂相互作用决定的,而不仅仅是TOPS.
- 该研究为优化嵌入式平台上的对象检测部署提供了实用见解.
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