论文标题
执行AI推理时，估计异质设备的功耗
Estimating the Power Consumption of Heterogeneous Devices when performing AI Inference
论文作者
论文摘要
现代生活是由连接到互联网的电子设备驱动的。新兴的研究领域（IoT）的新兴研究领域已经流行，就像连接设备数量稳步增加一样。由于这些设备中的许多用于执行CV任务，因此必须了解其功耗与性能相比。我们在执行对象分类时报告了NVIDIA JETSON NANO板的功耗概况和分析。作者对使用Yolov5模型进行了有关每帧功耗和每秒帧输出的广泛分析。结果表明，Yolov5N在吞吐量（即12.34 fps）和低功耗（即0.154 MWH/Frafe）方面优于其他Yolov5变体。
Modern-day life is driven by electronic devices connected to the internet. The emerging research field of the Internet-of-Things (IoT) has become popular, just as there has been a steady increase in the number of connected devices. Since many of these devices are utilised to perform CV tasks, it is essential to understand their power consumption against performance. We report the power consumption profile and analysis of the NVIDIA Jetson Nano board while performing object classification. The authors present an extensive analysis regarding power consumption per frame and the output in frames per second using YOLOv5 models. The results show that the YOLOv5n outperforms other YOLOV5 variants in terms of throughput (i.e. 12.34 fps) and low power consumption (i.e. 0.154 mWh/frame).
