Trade-Off Analysis of Pruning Methods for Compact Neural Networks on Embedded Devices

Sebastiaan B.H.C. Hofstee*, Duc V. Le

*Corresponding author for this work

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

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Abstract

Pruning of neural networks is a technique often used to reduce the size of a machine learning model, as well as to reduce the computation cost for model inference. This research provides an analysis on four current pruning techniques that theoretically efficiently reduce the machine learning model size, where efficiency is defined by the relation between the compression of the model and the accuracy of the model. Furthermore, this research will assess in what way these four neural network pruning techniques affect the total energy consumption during model inference on a Raspberry Pi 4B board, applied to MobileNetV2, a machine learning model architecture optimized for image classification on embedded devices. Lastly, the research will analyze the trade-offs between energy consumption, model size and model accuracy for each of the assessed pruning algorithms applied to one of the most commonly used neural network architectures, MobileNetV2, on a Raspberry Pi 4B prototyping board. The research is expected to provide engineers a reference providing guidance upon deciding what pruning technique to use for a machine learning model to be deployed on an embedded device.

Original languageEnglish
Title of host publicationInternet of Things. IoT through a Multi-disciplinary Perspective
Subtitle of host publication5th IFIP International Cross-Domain Conference, IFIPIoT 2022, Amsterdam, The Netherlands, October 27–28, 2022, Proceedings
EditorsLuis M. Camarinha-Matos, Luis Ribeiro, Leon Strous
Place of PublicationCham
PublisherSpringer
Pages274-292
Number of pages19
ISBN (Electronic)978-3-031-18872-5
ISBN (Print)978-3-031-18871-8, 978-3-031-18874-9
DOIs
Publication statusPublished - 2022
Event5th IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2022 - Amsterdam, Netherlands
Duration: 27 Oct 202228 Oct 2022
Conference number: 5

Publication series

NameIFIP Advances in Information and Communication Technology
PublisherIFIP
Volume665
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Conference

Conference5th IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2022
Abbreviated titleIFIPIoT 2022
Country/TerritoryNetherlands
CityAmsterdam
Period27/10/2228/10/22

Keywords

  • Deep learning
  • Efficiency
  • Embedded devices
  • Energy consumption
  • Machine Learning (ML)
  • Neural networks
  • Pruning
  • 2024 OA procedure

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