Analyzing and increasing the reliability of convolutional neural networks on GPUs
Graphics processing units (GPUs) are playing a critical role in convolutional neural networks
(CNNs) for image detection. As GPU-enabled CNNs move into safety-critical environments …
(CNNs) for image detection. As GPU-enabled CNNs move into safety-critical environments …
Artificial neural networks for space and safety-critical applications: Reliability issues and potential solutions
P Rech - IEEE Transactions on Nuclear Science, 2024 - ieeexplore.ieee.org
Machine learning is among the greatest advancements in computer science and
engineering and is today used to classify or detect objects, a key feature in autonomous …
engineering and is today used to classify or detect objects, a key feature in autonomous …
A Survey on Failure Analysis and Fault Injection in AI Systems
The rapid advancement of Artificial Intelligence (AI) has led to its integration into various
areas, especially with Large Language Models (LLMs) significantly enhancing capabilities …
areas, especially with Large Language Models (LLMs) significantly enhancing capabilities …
Soft error effects on arm microprocessors: Early estimations versus chip measurements
PR Bodmann, G Papadimitriou… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Extensive research efforts are being carried out to evaluate and improve the reliability of
computing devices either through beam experiments or simulation-based fault injection …
computing devices either through beam experiments or simulation-based fault injection …
Resiliency of automotive object detection networks on GPU architectures
Safety is the most important aspect of an autonomous driving platform. Deep neural
networks (DNNs) play an increasingly critical role in localization, perception, and control in …
networks (DNNs) play an increasingly critical role in localization, perception, and control in …
Revealing gpus vulnerabilities by combining register-transfer and software-level fault injection
The complexity of both hardware and software makes GPUs reliability evaluation extremely
challenging. A low level fault injection on a GPU model, despite being accurate, would take …
challenging. A low level fault injection on a GPU model, despite being accurate, would take …
A selective mitigation technique of soft errors for dnn models used in healthcare applications: Densenet201 case study
Deep neural networks (DNNs) have been successfully deployed in widespread domains,
including healthcare applications. DenseNet201 is a new DNN architecture used in …
including healthcare applications. DenseNet201 is a new DNN architecture used in …
Experimental and analytical study of xeon phi reliability
We present an in-depth analysis of transient faults effects on HPC applications in Intel Xeon
Phi processors based on radiation experiments and high-level fault injection. Besides …
Phi processors based on radiation experiments and high-level fault injection. Besides …
Reliability evaluation of mixed-precision architectures
Novel computing architectures offer the possibility to execute float point operations with
different precisions. The execution of reduced precision operations, when acceptable for …
different precisions. The execution of reduced precision operations, when acceptable for …
Combining architectural simulation and software fault injection for a fast and accurate CNNs reliability evaluation on GPUs
Graphic Processing Units (GPUs) are commonly used to accelerate Convolutional Neural
Networks (CNNs) for object detection and classification. As CNNs are employed in safety …
Networks (CNNs) for object detection and classification. As CNNs are employed in safety …