<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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, &amp; desktop computer. Results are derived for performance analysis in terms of classification accuracy and hardware response for each hardware configuration.</p>
</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.

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

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

With the advancement in technological world, the technologies like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are gaining more...

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

Data publikacji: 22 wrz 2022

Zakres stron: 68 - 81

Otrzymano: 10 sty 2022

Przyjęty: 08 cze 2022

DOI: https://doi.org/10.2478/cait-2022-0028

Słowa kluczowe
Embedded devices, Nvidia Jetson nano board, deep learning, image classification, hardware performance, Cuda core

© 2022 Lalit Kumar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Data publikacji: 22 wrz 2022

Zakres stron: 68 - 81

Otrzymano: 10 sty 2022

Przyjęty: 08 cze 2022

DOI: https://doi.org/10.2478/cait-2022-0028

Słowa kluczoweEmbedded devices, Nvidia Jetson nano board, deep learning, image classification, hardware performance, Cuda core

© 2022 Lalit Kumar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Embedded devices, Nvidia Jetson nano board, deep learning, image classification, hardware performance, Cuda core