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.

中文翻译：

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使用英特尔 OpenVINO 工具套件在边缘部署智能排队系统。

最近计算能力的提高和专业架构的发展导致在边缘执行机器学习，尤其是推理的可能性。OpenVINO 是一个基于卷积神经网络的工具包，有助于将计算机视觉算法和深度学习神经网络快速开发到视觉应用程序中，并使其能够跨硬件平台轻松异构执行。智能队列管理可能是任何部门成功的关键。在本文中，我们专注于边缘部署，以使所有人都可以访问智能排队系统 (SQS)，同时提供在廉价设备上运行它的能力。这使其能够在零售店、公共交通设施或工厂可能已经拥有的现有计算机上运行排队系统深度学习算法，从而大大降低了部署这种系统的成本。SQS 演示了如何在边缘创建视频 AI 解决方案。我们通过在多个边缘设备上进行测试来验证我们的结果，即 CPU、集成边缘图形处理单元 (iGPU)、视觉处理单元 (VPU) 和现场可编程门阵列 (FPGA)。实验结果表明，在边缘部署 SQS 是非常有前景的。