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Deploying a smart queuing system on edge with Intel OpenVINO toolkit

Authors
  • Dagli, Rishit1
  • Eken, Süleyman2
  • 1 Thakur International School Mumbai, Mumbai, India
  • 2 Kocaeli University,
Type
Published Article
Journal
Soft Computing
Publisher
Springer-Verlag
Publication Date
Jun 28, 2021
Pages
1–13
Identifiers
DOI: 10.1007/s00500-021-05891-2
PMCID: PMC8238477
Source
PubMed Central
Keywords
Disciplines
  • Data Analytics and Machine Learning
License
Unknown

Abstract

Recent increases in computational power and the development of specialized architecture led to the possibility to perform machine learning, especially inference, on the edge. OpenVINO is a toolkit based on convolutional neural networks that facilitates fast-track development of computer vision algorithms and deep learning neural networks into vision applications, and enables their easy heterogeneous execution across hardware platforms. A smart queue management can be the key to the success of any sector. In this paper, we focus on edge deployments to make the smart queuing system (SQS) accessible by all also providing ability to run it on cheap devices. This gives it the ability to run the queuing system deep learning algorithms on pre-existing computers which a retail store, public transportation facility or a factory may already possess, thus considerably reducing the cost of deployment of such a system. SQS demonstrates how to create a video AI solution on the edge. We validate our results by testing it on multiple edge devices, namely CPU, integrated edge graphic processing unit (iGPU), vision processing unit (VPU) and field-programmable gate arrays (FPGAs). Experimental results show that deploying a SQS on edge is very promising.

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