在多神经处理单元系统中优化实时对象检测.
Sehyeon Oh1,2, Yongin Kwon1,2, Jemin Lee1,2
1Department of Artificial Intelligence, University of Science and Technology, Daejeon 34113, Republic of Korea.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
这项研究优化了使用神经处理单元 (NPU) 的实时对象检测. 利用双缓冲和多NPU任务分配可以显著提高吞吐量并减少与仅CPU系统相比的延迟.
科学领域:
- 计算机视觉 计算机视觉
- 硬件加速器 硬件加速器
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 实时对象检测需要高吞吐量和低延迟.
- 神经处理单元 (NPU) 为深度学习任务提供了卓越的能源效率和并行处理.
- 在实时应用中,NPU对于降低延迟和提高速度至关重要.
研究的目的:
- 使用YOLOv3和Neubla的Antara NPU构建一个实时物体检测系统.
- 为基于NPU的对象检测提出和评估性能优化策略.
- 分析缓冲和多NPU配置对系统性能的影响.
主要方法:
- 在 Antara NPU 上使用 YOLOv3 实现了实时物体检测系统.
- 采用双缓冲来实现CPU和NPU之间的并行数据处理.
- 利用基于队列的处理来在多个NPU上分配任务.
- 应用阿姆达尔定律来分析多NPU环境中的性能限制.
主要成果:
- 与单独CPU相比,双缓冲的NPU集成提高了3.35倍的吞吐量.
- 多NPU环境实现了4.81倍的吞吐量增加.
- 延迟时间在单/双缓冲时减少了1.6倍,在多NPU设置时减少了1.18倍.
- 对象检测准确度仍然很高 (NPU上的31.8 mAP与CPU上的31.4 mAP).
结论:
- 与双缓冲和多NPU任务分配相结合的NPU加速,显著提高了实时对象检测性能.
- 优化的NPU利用提供了大量的吞吐量增长和延迟减少,而不会影响准确性.
- 提出的方法证明了专用硬件对要求高的AI应用程序的有效性.
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