# Estimating the Power Consumption of Heterogeneous Devices when performing AI Inference

arXiv: https://arxiv.org/abs/2207.06150 · HTML: https://mlsanity.com/paper/2207.06150/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video](https://mlsanity.com/paper/1709.05943/) (2017)
- [Efficient Sparse-Winograd Convolutional Neural Networks](https://mlsanity.com/paper/1802.06367/) (2018)
- [Deep Learning on Mobile Devices - A Review](https://mlsanity.com/paper/1904.09274/) (2019)

## Built on by (most cited first, 1 of 1)

- [Low-power Ship Detection in Satellite Images Using Neuromorphic Hardware](https://mlsanity.com/paper/2406.11319/) (2024)

## 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).