Analyzing the instructions vulnerability of dense convolutional network on GPUS
Recently, Deep Neural Networks (DNNs) have been increasingly deployed in various healthcare applications, which are considered safety-critical applications. Thus, the reliability of these DNN models should be remarkably high, because even a small error in healthcare applications can lead to injury o...
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/30696/ http://umpir.ump.edu.my/id/eprint/30696/1/Analyzing%20the%20instructions%20vulnerability%20of%20dense%20convolutional%20network%20on%20GPUS.pdf |
| _version_ | 1848823578066157568 |
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| author | Khalid, Adam Izzeldin, I. Mohd Ibrahim, Younis |
| author_facet | Khalid, Adam Izzeldin, I. Mohd Ibrahim, Younis |
| author_sort | Khalid, Adam |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Recently, Deep Neural Networks (DNNs) have been increasingly deployed in various healthcare applications, which are considered safety-critical applications. Thus, the reliability of these DNN models should be remarkably high, because even a small error in healthcare applications can lead to injury or death. Due to the high computations of the DNN models, DNNs are often executed on the Graphics Processing Units (GPUs). However, the GPUs have been reportedly impacted by soft errors, which are extremely serious issues in the healthcare applications. In this paper, we show how the fault injection can provide a deeper understanding of DenseNet201 model instructions vulnerability on the GPU. Then, we analyze vulnerable instructions of the DenseNet201 on the GPU. Our results show that the most significant vulnerable instructions against soft errors PR, STORE, FADD, FFMA, SETP and LD can be reduced from 4.42% to 0.14% of injected faults, after we applied our mitigation strategy. |
| first_indexed | 2025-11-15T02:59:21Z |
| format | Article |
| id | ump-30696 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:59:21Z |
| publishDate | 2021 |
| publisher | Institute of Advanced Engineering and Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-306962021-10-14T02:18:38Z http://umpir.ump.edu.my/id/eprint/30696/ Analyzing the instructions vulnerability of dense convolutional network on GPUS Khalid, Adam Izzeldin, I. Mohd Ibrahim, Younis QA76 Computer software TA Engineering (General). Civil engineering (General) Recently, Deep Neural Networks (DNNs) have been increasingly deployed in various healthcare applications, which are considered safety-critical applications. Thus, the reliability of these DNN models should be remarkably high, because even a small error in healthcare applications can lead to injury or death. Due to the high computations of the DNN models, DNNs are often executed on the Graphics Processing Units (GPUs). However, the GPUs have been reportedly impacted by soft errors, which are extremely serious issues in the healthcare applications. In this paper, we show how the fault injection can provide a deeper understanding of DenseNet201 model instructions vulnerability on the GPU. Then, we analyze vulnerable instructions of the DenseNet201 on the GPU. Our results show that the most significant vulnerable instructions against soft errors PR, STORE, FADD, FFMA, SETP and LD can be reduced from 4.42% to 0.14% of injected faults, after we applied our mitigation strategy. Institute of Advanced Engineering and Science 2021-10 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/30696/1/Analyzing%20the%20instructions%20vulnerability%20of%20dense%20convolutional%20network%20on%20GPUS.pdf Khalid, Adam and Izzeldin, I. Mohd and Ibrahim, Younis (2021) Analyzing the instructions vulnerability of dense convolutional network on GPUS. International Journal of Electrical and Computer Engineering (IJECE), 11 (5). pp. 4481-4488. ISSN 2088-8708 (Print); 2722-2578 (Online). (Published) http://ijece.iaescore.com/index.php/IJECE/article/view/24607/15136 http://doi.org/10.11591/ijece.v11i5.pp4481-4488 |
| spellingShingle | QA76 Computer software TA Engineering (General). Civil engineering (General) Khalid, Adam Izzeldin, I. Mohd Ibrahim, Younis Analyzing the instructions vulnerability of dense convolutional network on GPUS |
| title | Analyzing the instructions vulnerability of dense convolutional network on GPUS |
| title_full | Analyzing the instructions vulnerability of dense convolutional network on GPUS |
| title_fullStr | Analyzing the instructions vulnerability of dense convolutional network on GPUS |
| title_full_unstemmed | Analyzing the instructions vulnerability of dense convolutional network on GPUS |
| title_short | Analyzing the instructions vulnerability of dense convolutional network on GPUS |
| title_sort | analyzing the instructions vulnerability of dense convolutional network on gpus |
| topic | QA76 Computer software TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/30696/ http://umpir.ump.edu.my/id/eprint/30696/ http://umpir.ump.edu.my/id/eprint/30696/ http://umpir.ump.edu.my/id/eprint/30696/1/Analyzing%20the%20instructions%20vulnerability%20of%20dense%20convolutional%20network%20on%20GPUS.pdf |