通过Intel神经计算棒2加速图像分类和对象跟踪,并通过Raspberry Pi 4B上的电力效率评估来加速图像分类和对象跟踪
Tianyu Gao1, Jozsef Suto1,2
1Department of Informatics Systems and Networks, Faculty of Informatics, University of Debrecen, Kassai Street 26, 4028 Debrecen, Hungary.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
英特尔神经计算棒2 (NCS2) 在Raspberry Pi 4B上显著加速深度学习,将图像识别提高了400%,对象跟踪提高了1400%. 这使得嵌入式系统中的高效实时AI成为可能.
科学领域:
- 计算机视觉 计算机视觉
- 嵌入式系统人工智能嵌入式系统
- 硬件加速器 硬件加速器
背景情况:
- 复杂的神经网络的实时运行对于嵌入式系统至关重要.
- 在工业应用中部署深度学习需要具有成本效益的解决方案.
- 英特尔神经计算棒2 (NCS2) 提供了边缘AI加速的潜力.
研究的目的:
- 在树派4B上评估NCS2的效率和功耗.
- 评估NCS2在加速图像分类和对象跟踪方面的表现.
- 用现实世界的应用数据来补充OpenVINOTM文档.
主要方法:
- 使用Raspberry Pi 4B平台与英特尔NCS2.2.一起使用
- 使用单一模型进行图像识别测试.
- 通过使用深度SORT算法对两个模型进行实时对象跟踪测试.
- 评估了不同型号和条件的性能变化.
主要成果:
- NCS2提高了图像识别性能大约400%.
- 实时对象跟踪性能提高了1200%至1400%.
- 在图像识别方面达到50 FPS以上,在对象跟踪方面达到20 FPS以上.
- 通过NCS2.2,观察到200%至400%的功率效益.
结论:
- 在受约束的硬件上,NCS2为AI任务提供了实质性的性能增强.
- 树派4B和NCS2的组合对于实时嵌入式AI应用程序是有效的.
- 显著的功率效率改进使NCS2成为边缘计算的可行解决方案.
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