分析边缘计算设备用于部署嵌入式AI
Asier Garcia-Perez1, Raúl Miñón1, Ana I Torre-Bastida2
1Digital, TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Álava Albert Einstein 28, 01510 Vitoria-Gasteiz, Álava, Spain.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
边缘计算在源头附近处理数据,这对物联网至关重要. 使用像Tensor处理单元这样的人工智能加速器显著提高了边缘设备的性能,超出了仅CPU的能力.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络工程 网络工程
背景情况:
- 物联网 (IoT) 设备的扩散产生了大量的数据.
- 传统的云计算面临着对物联网应用的延迟,效率和实时响应的局限性.
- 边缘计算成为处理数据更接近源的解决方案,解决云计算的局限性.
研究的目的:
- 分析和比较各种边缘计算设备的性能,以部署人工智能算法.
- 评估人工智能加速器,特别是Tensor处理单元 (TPU) 对边缘设备性能的影响.
- 根据特定的AI要求指导选择最佳边缘设备.
主要方法:
- 进行详细的实验,比较多个边缘设备,AI模型和性能指标.
- 在选定的边缘计算硬件上部署人工智能算法.
- 观察和测量人工智能加速器 (如TPU) 的性能.
主要成果:
- 在仅使用其CPU时,Jetson Nano表现出强的性能.
- 集成Tensor处理单元 (TPU) 显著提高了边缘设备的性能.
- 具体的性能增长取决于所使用的边缘设备,AI模型和加速器.
结论:
- 边缘计算与人工智能相结合,为实时数据处理和自主决策提供了强大的解决方案.
- 虽然像Jetson Nano这样的设备上的基于CPU的处理是可行的,但像TPU这样的AI加速器提供了实质性的性能改进.
- 边缘设备的选择和AI加速器的包含对于在网络边缘满足苛刻的AI应用要求至关重要.
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