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Estimating the Power Consumption of Heterogeneous Devices when performing AI Inference

arXiv 2207.06150 ↗First posted 13 Jul 2022cs.AR cs.AI cs.CVcited by 1

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Abstract (arXiv)

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).

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 3 of its 21 references that are in the map. First posted on arXiv 13 Jul 2022.

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3 of 21 references.

References · 3

  1. 2017Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Videocited by 30
  2. 2018Efficient Sparse-Winograd Convolutional Neural Networkscited by 20
  3. 2019Deep Learning on Mobile Devices - A Reviewcited by 16

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