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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/35434/ 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 |
| _version_ | 1848824777541681152 |
|---|---|
| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:18:25Z |
| format | Article |
| id | ump-35434 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:18:25Z |
| publishDate | 2021 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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. (Published) |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/35434/ 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 |