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The impact of the soft errors in convolutional neural network on GPUS: Alexnet as case study

Convolutional Neural Networks (CNNs) have been increasingly deployed in many applications, including safety critical system such as healthcare and autonomous vehicles. Meanwhile, the vulnerability of CNN model to soft errors (e.g., caused by radiation mduced) rapidly increases, thus reliability is c...

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Main Authors: Adam Ismail Hammad, Khalid, Mohamed Abdelaziz, Izzeldin Ibrahim, Younis, Younis M.
Format: Article
Language:English
Published: Elsevier 2021
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/35434/1/The%20impact%20of%20the%20soft%20errors%20in%20convolutional%20neural%20network%20on%20GPUS_Alexnet%20as%20case%20study.pdf
http://umpir.ump.edu.my/id/eprint/35434/
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spelling my.ump.umpir.354342022-10-26T03:31:47Z http://umpir.ump.edu.my/id/eprint/35434/ The impact of the soft errors in convolutional neural network on GPUS: Alexnet as case study Adam Ismail Hammad, Khalid Mohamed Abdelaziz, Izzeldin Ibrahim Younis, Younis M. QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Convolutional Neural Networks (CNNs) have been increasingly deployed in many applications, including safety critical system such as healthcare and autonomous vehicles. Meanwhile, the vulnerability of CNN model to soft errors (e.g., caused by radiation mduced) rapidly increases, thus reliability is crucial especially in real-tmie system. There are many traditional techniques for miprove the reliability of the system, e.g.. Triple Modular Redundancy, but these techniques incur high overheads, which makes them hard to deploy. In tins paper, we experimentally evaluate the vulnerable parts of Alexnet mode (e.g., fault mjector). Results show that FADD and LD are the top vulnerable mstructions against soft errors for Alexnet model, both mstruetions generate at least 84% of injected faults as SDC errors. Thus, these the only parts of the Alexnet model that need to be hardened mstead of usmg fully duplication solutions. Elsevier 2021 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/35434/1/The%20impact%20of%20the%20soft%20errors%20in%20convolutional%20neural%20network%20on%20GPUS_Alexnet%20as%20case%20study.pdf Adam Ismail Hammad, Khalid and Mohamed Abdelaziz, Izzeldin Ibrahim and Younis, Younis M. (2021) The impact of the soft errors in convolutional neural network on GPUS: Alexnet as case study. Procedia Computer Science, 182. pp. 89-94. ISSN 1877-0509
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Adam Ismail Hammad, Khalid
Mohamed Abdelaziz, Izzeldin Ibrahim
Younis, Younis M.
The impact of the soft errors in convolutional neural network on GPUS: Alexnet as case study
description Convolutional Neural Networks (CNNs) have been increasingly deployed in many applications, including safety critical system such as healthcare and autonomous vehicles. Meanwhile, the vulnerability of CNN model to soft errors (e.g., caused by radiation mduced) rapidly increases, thus reliability is crucial especially in real-tmie system. There are many traditional techniques for miprove the reliability of the system, e.g.. Triple Modular Redundancy, but these techniques incur high overheads, which makes them hard to deploy. In tins paper, we experimentally evaluate the vulnerable parts of Alexnet mode (e.g., fault mjector). Results show that FADD and LD are the top vulnerable mstructions against soft errors for Alexnet model, both mstruetions generate at least 84% of injected faults as SDC errors. Thus, these the only parts of the Alexnet model that need to be hardened mstead of usmg fully duplication solutions.
format Article
author Adam Ismail Hammad, Khalid
Mohamed Abdelaziz, Izzeldin Ibrahim
Younis, Younis M.
author_facet Adam Ismail Hammad, Khalid
Mohamed Abdelaziz, Izzeldin Ibrahim
Younis, Younis M.
author_sort Adam Ismail Hammad, Khalid
title The impact of the soft errors in convolutional neural network on GPUS: Alexnet as case study
title_short The impact of the soft errors in convolutional neural network on GPUS: Alexnet as case study
title_full The impact of the soft errors in convolutional neural network on GPUS: Alexnet as case study
title_fullStr The impact of the soft errors in convolutional neural network on GPUS: Alexnet as case study
title_full_unstemmed The impact of the soft errors in convolutional neural network on GPUS: Alexnet as case study
title_sort impact of the soft errors in convolutional neural network on gpus: alexnet as case study
publisher Elsevier
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/35434/1/The%20impact%20of%20the%20soft%20errors%20in%20convolutional%20neural%20network%20on%20GPUS_Alexnet%20as%20case%20study.pdf
http://umpir.ump.edu.my/id/eprint/35434/
_version_ 1748180701995335680
score 13.211869

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