DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices

Stephan Patrick Baller, Anshul Jindal, Mohak Chadha, Michael Gerndt

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

59 Scopus citations

Abstract

EdgeAI (Edge computing based Artificial Intelligence) has been most actively researched for the last few years to handle variety of massively distributed AI applications to meet up the strict latency requirements. Meanwhile, many companies have released edge devices with smaller form factors (low power consumption and limited resources) like the popular Raspberry Pi and Nvidia's Jetson Nano for acting as compute nodes at the edge computing environments. Although the edge devices are limited in terms of computing power and hardware resources, they are powered by accelerators to enhance their performance behavior. Therefore, it is interesting to see how AI-based Deep Neural Networks perform on such devices with limited resources. In this work, we present and compare the performance in terms of inference time and power consumption of the four SoCs: Asus Tinker Edge R, Raspberry Pi 4, Google Coral Dev Board, Nvidia Jetson Nano, and one microcontroller: Arduino Nano 33 BLE, on different deep learning models and frameworks. We also provide a method for measuring power consumption, inference time and accuracy for the devices, which can be easily extended to other devices. Our results showcase that, for Tensorflow based quantized model, the Google Coral Dev Board delivers the best performance, both for inference time and power consumption. For a low fraction of inference computation time, i.e. less than 29.3% of the time for MobileNetV2, the Jetson Nano performs faster than the other devices.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Cloud Engineering, IC2E 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages20-30
Number of pages11
ISBN (Electronic)9781665449700
DOIs
StatePublished - 2021
Event9th IEEE International Conference on Cloud Engineering, IC2E 2021 - Virtual, Online, United States
Duration: 4 Oct 20218 Oct 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Cloud Engineering, IC2E 2021

Conference

Conference9th IEEE International Conference on Cloud Engineering, IC2E 2021
Country/TerritoryUnited States
CityVirtual, Online
Period4/10/218/10/21

Keywords

  • deep learning
  • edge computing
  • edge devices
  • inference time
  • performance benchmark
  • power consumption
  • power prediction

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