Yapay Sinir Ağlarının Donanım Hataları Altındaki Hata Toleransı ve Zafiyetinin Değerlendirilmesi

Year 2026, Volume: 14 Issue: 2 , 537 - 550 , 19.04.2026

Hatice Aktaş Aydın , Albert Njoroge Kahira , Gülay Yalçın , Osman Ünsal

https://doi.org/10.29130/dubited.1793166

https://izlik.org/JA49BT43WU

Abstract

Yapay Sinir Ağları (YSA), yapay zekaya olan ilgi ve gelişmelerin artması, Yüksek Performanslı Hesaplama (YBH) sistemlerinin sunduğu hesaplama gücünün artması nedeniyle tekrar popülerlik kazanmıştır. Sinir ağı uygulamaları büyük veri merkezlerinde ve YBH sistemlerinde kullanıldığından, bu sistemlerde yaygın olan kayıtlarda ve bellek yapılarında bit kayması gibi benzer güvenilirlik sorunlarıyla karşı karşıyadırlar. Bu nedenle, sistem maliyetini önemli ölçüde artırabilen özel sağlamlık ve koruma mekanizmaları gerektirirler. Ancak, donanım arızalarının YBH uygulamalarının farklı bileşenleri üzerindeki etkisini anlamak, hangi parçaların daha savunmasız olduğunu ve daha yüksek güvenilirlik gerektirdiğini belirlemeye yardımcı olabilir. Bu çalışmada, YBH sistemlerinde ve büyük ölçekli veri merkezlerinde çalıştırıldığında donanım arızalarının YBH uygulamaları üzerindeki etkileri değerlendirilmiş ve böylece güvenilirlik maliyetlerinin düşürülmesi hedeflenmiştir. Geleneksel tekniklerle gerçekleştirilen hata enjeksiyon deneyleri YBH uygulamaları için oldukça zaman alıcı olabilir. Bu nedenle, bu tür uygulamalarda hata enjeksiyon süresini azaltmak için bir yöntem sunulmuştur. CPU tabanlı (Intel Xeon) ve GPU tabanlı (NVIDIA V100) yüksek performanslı bilgi işlem (HPC) sistemlerinde çalışan Yapay Sinir Ağı (YSA) uygulamaları üzerinde donanım arızalarının etkilerini değerlendirdiğimizde, sonuçlarımız YSA'ların bazı donanım arızalarına, özellikle belirli katmanlarda ve mimari kayıtlarda oluşan arızalara karşı savunmasız olduğunu göstermektedir.

Keywords

Hata Toleransı , güvenilirlik , makine öğrenmesi , yapay sinir ağları , yapay zeka

Evaluating the Fault Tolerance and Vulnerability of Artificial Neural Networks Under Hardware Errors

https://izlik.org/JA49BT43WU

Abstract

Artificial Neural Networks (ANN) have gained popularity again due to the increasing interest and developments in artificial intelligence, as well as the increased computational power offered by High Performance Computing (HPC) systems. Since neural network applications are used in large data centers and HPC systems, they face similar reliability issues such as bit slippage in registers and memory structures that are common in these systems. Therefore, they require special robustness and protection mechanisms that can significantly increase the system cost. However, understanding the impact of hardware failures on different components of ANN applications can help determine which parts are more vulnerable and require higher reliability. In this study, the effects of hardware faults on ANN applications when they are run in HPC systems and large-scale data centers are evaluated, and thus, the reliability costs are aimed to be reduced. Fault injection experiments performed with traditional techniques can be quite time-consuming for ANN applications. Therefore, a method is presented to reduce the fault injection time in such applications. When we evaluate the effects of hardware faults on Artificial Neural Network (ANN) applications running on CPU-based (Intel Xeon) and GPU-based (NVIDIA V100) high-performance computing (HPC) systems, our results show that ANNs are vulnerable to some hardware faults, especially those occurring in certain layers and architectural registers.

Keywords

Fault tolerance , Reliability , Machine learning , Artificial neural networks , Artificial intelligence

Ethical Statement

This study does not involve human or animal participants. All procedures followed scientific and ethical principles, and all referenced studies are appropriately cited.

Supporting Institution

This research received no external funding.

Thanks

The authors declare that there are no acknowledgements.

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