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: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021
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| Subjects: | |
| 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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| Summary: | 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. |
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