As data being produced by IoT applications continues to explode, there is a growing need to bring computing power closer to the source of the data to meet the response time, power dissipation and cost goals of performance-critical applications in various domains like the Industrial Internet of Things (IIoT), Automated Driving, Medical Imaging or Surveillance among others. This paper proposes a data collection and utilization framework that allows runtime platform and application data to be sent to an edge and cloud system via data collection agents running close to the platform. Agents are connected to a cloud system able to train AI models to improve overall energy efficiency of an AI application executed on an edge platform. In the implementation part, we show the benefits of FPGA-based platform for the task of object detection. Furthermore, we show that it is feasible to collect relevant data from an FPGA platform, transmit the data to a cloud system for processing and receiving feedback actions to execute an edge AI application energy efficiently. As future work, we foresee the possibility to train, deploy and continuously improve a base model able to efficiently adapt the execution of edge applications.
翻译:由于IoT应用程序产生的数据继续爆炸,越来越需要使计算能力更接近数据源,以满足诸如Things工业互联网(IIoT)、自动驾驶、医疗成像或监视等不同领域的性能关键应用的反应时间、耗电量和成本目标。本文件提议了一个数据收集和使用框架,使运行时平台和应用数据能够通过离平台很近的数据收集代理器传送到边缘和云层系统。代理器与一个能够培训AI模型的云层系统相连,以提高在边缘平台执行的AI应用程序的总体能效。在执行部分,我们展示了基于FPGA的平台对物体探测任务的好处。此外,我们表明从FPGA平台收集相关数据、将数据传送到云层系统以便处理和接收反馈行动以便高效地执行边缘AI应用能源。作为未来的工作,我们预见了培训、部署和不断改进能够高效调整边缘应用执行的基础模型的可能性。