一个车辆边缘云框架用于微调深度学习模型的计算分析.
M Jalal Khan1,2, Manzoor Ahmed Khan1,2, Sherzod Turaev1
1College of Information Technology, United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
为自动驾驶汽车部署深度学习模型需要仔细考虑计算层. 这项研究分析了YOLOv8在车辆边缘云架构上的性能,发现了一致的准确性,但根据硬件和环境的推断时间有所不同.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 合作,互联和自动化移动 (CCAM) 基础设施对于城市地区的自动驾驶汽车 (AV) 环境感知至关重要.
- 机器学习 (ML) 和深度学习 (DL) 模型的高效部署对于提高CCAM框架内的AV性能至关重要.
- 计算处理层和特定DL模型的选择会影响复杂环境中的AV能力.
研究的目的:
- 为在CCAM基础架构中部署DL模型提出一个计算框架.
- 分析定制训练的YOLOv8 DL模型在车辆边缘云架构中的多种设备和层的有效性.
- 调查DL模型准确性和在分层框架内部署期间的执行时间之间的权衡.
主要方法:
- 开发了一个用于在车辆边缘云层架构中部署DL模型的计算框架.
- 在各种计算层和设备中评估了一种定制训练的YOLOv8 DL模型.
- 在不同的环境条件和硬件配置下测量性能指标,包括精度 (mAP@0.5) 和推断时间.
主要成果:
- 在框架的任何层中,DL模型性能指标 (例如0.842 mAP@0.5) 在不同的设备类型中保持一致.
- 在不同的环境条件下,对象检测任务的推理时间减少.
- 特定的硬件配置显示,推断时间显著减少:与拉斯伯雷Pi (非GPU) 相比,Jetson AGX (非GPU) 减少了72%的时间,与Jetson AGX ARMv8 (非GPU) 相比,Jetson AGX Xavier (GPU) 减少了90%的时间.
结论:
- 计算层和设备的选择在CCAM系统中显著影响DL模型推断时间,而不是它的准确性.
- 研究人员和从业人员可以利用这些发现来优化DL模型部署用于生产AV系统.
- 选择合适的硬件和部署环境是平衡自动驾驶系统性能和效率的关键.
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