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Analyzing the reliability of convolutional neural networks on GPUs : GoogLeNet as a case study

Convolutional Neural Networks (CNNs) are used for tasks such as object recognition. Once a CNN model is used in a radiative environment, reliability of the system against soft errors is a crucial issue, especially in safety-critical and high-performance applications that bound with real-time respons...

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Main Authors: Ibrahim, Younis Mohammed, Wang, Haibin, Khalid, Adam
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/42444/1/Analyzing%20the%20reliability%20of%20convolutional%20neural%20networks%20on%20GPUs.pdf
http://umpir.ump.edu.my/id/eprint/42444/2/Analyzing%20the%20reliability%20of%20convolutional%20neural%20networks%20on%20GPUs_GoogLeNet%20as%20a%20case%20study_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42444/
https://doi.org/10.1109/ICCIT-144147971.2020.9213804
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spelling my.ump.umpir.424442024-12-02T01:12:45Z http://umpir.ump.edu.my/id/eprint/42444/ Analyzing the reliability of convolutional neural networks on GPUs : GoogLeNet as a case study Ibrahim, Younis Mohammed Wang, Haibin Khalid, Adam T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Convolutional Neural Networks (CNNs) are used for tasks such as object recognition. Once a CNN model is used in a radiative environment, reliability of the system against soft errors is a crucial issue, especially in safety-critical and high-performance applications that bound with real-time response. Selectively-hardening techniques do improve the reliability of these systems. However, the hard question in selective techniques is "how to exclusively select code portions to harden, to safe the performance from being degraded". In this paper, we propose a comprehensive analysis methodology for CNN-based classification models to confidently determine the only vulnerable parts of the source code. To achieve this, we propose a technique, Layer Vulnerability Factor (LVF) and adopt another technique, Kernel Vulnerability Factor (KVF). We apply these techniques to GoogLeNet, which is a famous image classification model, to validate our methodology. We precisely identify the parts of the GoogLeNet model that need to be hardened instead of using expensive duplication solutions. Institute of Electrical and Electronics Engineers Inc. 2020-09-09 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42444/1/Analyzing%20the%20reliability%20of%20convolutional%20neural%20networks%20on%20GPUs.pdf pdf en http://umpir.ump.edu.my/id/eprint/42444/2/Analyzing%20the%20reliability%20of%20convolutional%20neural%20networks%20on%20GPUs_GoogLeNet%20as%20a%20case%20study_ABS.pdf Ibrahim, Younis Mohammed and Wang, Haibin and Khalid, Adam (2020) Analyzing the reliability of convolutional neural networks on GPUs : GoogLeNet as a case study. In: 2020 International Conference on Computing and Information Technology, ICCIT 2020. 2020 International Conference on Computing and Information Technology, ICCIT 2020 , 9 - 10 September 2020 , Tabuk. pp. 1-6. (9213804). ISBN 978-172812680-7 (Published) https://doi.org/10.1109/ICCIT-144147971.2020.9213804
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Ibrahim, Younis Mohammed
Wang, Haibin
Khalid, Adam
Analyzing the reliability of convolutional neural networks on GPUs : GoogLeNet as a case study
description Convolutional Neural Networks (CNNs) are used for tasks such as object recognition. Once a CNN model is used in a radiative environment, reliability of the system against soft errors is a crucial issue, especially in safety-critical and high-performance applications that bound with real-time response. Selectively-hardening techniques do improve the reliability of these systems. However, the hard question in selective techniques is "how to exclusively select code portions to harden, to safe the performance from being degraded". In this paper, we propose a comprehensive analysis methodology for CNN-based classification models to confidently determine the only vulnerable parts of the source code. To achieve this, we propose a technique, Layer Vulnerability Factor (LVF) and adopt another technique, Kernel Vulnerability Factor (KVF). We apply these techniques to GoogLeNet, which is a famous image classification model, to validate our methodology. We precisely identify the parts of the GoogLeNet model that need to be hardened instead of using expensive duplication solutions.
format Conference or Workshop Item
author Ibrahim, Younis Mohammed
Wang, Haibin
Khalid, Adam
author_facet Ibrahim, Younis Mohammed
Wang, Haibin
Khalid, Adam
author_sort Ibrahim, Younis Mohammed
title Analyzing the reliability of convolutional neural networks on GPUs : GoogLeNet as a case study
title_short Analyzing the reliability of convolutional neural networks on GPUs : GoogLeNet as a case study
title_full Analyzing the reliability of convolutional neural networks on GPUs : GoogLeNet as a case study
title_fullStr Analyzing the reliability of convolutional neural networks on GPUs : GoogLeNet as a case study
title_full_unstemmed Analyzing the reliability of convolutional neural networks on GPUs : GoogLeNet as a case study
title_sort analyzing the reliability of convolutional neural networks on gpus : googlenet as a case study
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/42444/1/Analyzing%20the%20reliability%20of%20convolutional%20neural%20networks%20on%20GPUs.pdf
http://umpir.ump.edu.my/id/eprint/42444/2/Analyzing%20the%20reliability%20of%20convolutional%20neural%20networks%20on%20GPUs_GoogLeNet%20as%20a%20case%20study_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42444/
https://doi.org/10.1109/ICCIT-144147971.2020.9213804
_version_ 1822924758679814144
score 13.23648

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