Hardware Response and Performance Analysis of Multicore Computing Systems for Deep Learning Algorithms

IF 1.2 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Cybernetics and Information Technologies Pub Date : 2022-09-01 DOI:10.2478/cait-2022-0028
Lalit Kumar, D. Singh
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引用次数: 1

Abstract

Abstract With the advancement in technological world, the technologies like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are gaining more popularity in many applications of computer vision like object classification, object detection, Human detection, etc., ML and DL approaches are highly compute-intensive and require advanced computational resources for implementation. Multicore CPUs and GPUs with a large number of dedicated processor cores are typically the more prevailing and effective solutions for the high computational need. In this manuscript, we have come up with an analysis of how these multicore hardware technologies respond to DL algorithms. A Convolutional Neural Network (CNN) model have been trained for three different classification problems using three different datasets. All these experimentations have been performed on three different computational resources, i.e., Raspberry Pi, Nvidia Jetson Nano Board, & desktop computer. Results are derived for performance analysis in terms of classification accuracy and hardware response for each hardware configuration.
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用于深度学习算法的多核计算系统的硬件响应和性能分析
摘要随着技术的进步,人工智能(AI)、机器学习(ML)和深度学习(DL)等技术在计算机视觉的许多应用中越来越受欢迎,如物体分类、物体检测、人体检测等。,ML和DL方法是高度计算密集型的,并且需要高级计算资源来实现。具有大量专用处理器核心的多核CPU和GPU通常是满足高计算需求的更普遍、更有效的解决方案。在这份手稿中,我们分析了这些多核硬件技术对DL算法的响应。卷积神经网络(CNN)模型已经使用三个不同的数据集针对三种不同的分类问题进行了训练。所有这些实验都是在三种不同的计算资源上进行的,即Raspberry Pi、Nvidia Jetson Nano Board和台式计算机。根据每个硬件配置的分类精度和硬件响应,导出用于性能分析的结果。
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来源期刊
Cybernetics and Information Technologies
Cybernetics and Information Technologies COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.20
自引率
25.00%
发文量
35
审稿时长
12 weeks
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