dubi̇ted Duzce University Journal of Science and Technology 2148-2446 Duzce University 10.29130/dubited.1793166 Machine Learning Algorithms Makine Öğrenmesi Algoritmaları Yapay Sinir Ağlarının Donanım Hataları Altındaki Hata Toleransı ve Zafiyetinin Değerlendirilmesi Evaluating the Fault Tolerance and Vulnerability of Artificial Neural Networks Under Hardware Errors https://orcid.org/0000-0002-1104-9307 Aktaş Aydın Hatice SİVAS BİLİM VE TEKNOLOJİ ÜNİVERSİTESİ https://orcid.org/0000-0002-1138-0577 Kahira Albert Njoroge AstraZeneca https://orcid.org/0000-0003-3929-8126 Yalçın Gülay ABDULLAH GUL UNIVERSITY https://orcid.org/0000-0002-0544-9697 Ünsal Osman Barcelona Supercomputing Center 04 19 2026 14 2 537 550 09 29 2025 02 24 2026 Copyright © 2013, Duzce University Journal of Science and Technology 2013 Duzce University Journal of Science and Technology

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.

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.

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