Lightweight Fault Detection and Management for Image Restoration

Bolchini, Cristiana; Cassano, Luca; Miele, Antonio; Biasielli, Matteo

doi:10.1109/dft50435.2020.9250844

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Lately, smart light-ABFT solutions have been introduced to further reduce the overhead of the mitigation solutions for GPUs [174] and FPGAs [151]. Other light-weighted solutions have also been proposed for generic image processing applications [175].…”

Section: A Softwarementioning

confidence: 99%

Artificial Neural Networks for Space and Safety-Critical Applications: Reliability Issues and Potential Solutions

Rech

2024

IEEE Trans. Nucl. Sci.

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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 vehicles. Since neural networks are heavily used in safety-critical applications, such as automotive and aerospace, their reliability must be paramount. However, the reliability evaluation of neural network systems is extremely challenging due to the complexity of the software, which is composed of hundreds of layers, and of the underlying hardware, typically a parallel device or an embedded accelerator.This paper reviews fundamental concepts of Artificial Intelligence, Deep Neural Network, and parallel computing device reliability. Then, the reliability studies that consider the radiation effects in the hardware, their propagation through the computing architecture, and their final impact on the software output are summarized. A detailed survey of the available strategies to measure the sensitivity of neural network frameworks and to observe fault propagation is given, together with a summary of the data obtained so far. Finally, a discussion on how to use the experimental evaluation to design effective and efficient hardening solutions for Artificial Neural Networks is provided. The available hardening solutions are critically reviewed, highlighting their benefits and drawbacks.

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Section: A Softwarementioning

confidence: 99%

Artificial Neural Networks for Space and Safety-Critical Applications: Reliability Issues and Potential Solutions

Rech

2024

IEEE Trans. Nucl. Sci.

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“…The work proposed in the current paper moves from [7] and, based on the preliminary results presented [14], proposes a new solution, based on AC to evaluate the entire image as usable or not, and at the same time to drastically reduce the execution time of the hardened application. Finally, it is worth mentioning the system-level fault detection scheme proposed in [24] where, similarly to [11], no replica is required; however, this scheme is tailored to a specific image processing algorithm and cannot be applied in general.…”

Section: Related Workmentioning

confidence: 99%

Approximation-based Fault Tolerance in Image Processing Applications

Biasielli¹,

Bolchini

Cassano

et al. 2021

IEEE Trans. Emerg. Topics Comput.

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Image processing applications exhibit an intrinsic degree of fault tolerance due to i) the redundant nature of images, and ii) the possible ability of the consumers of the application output to effectively carry out their task even when it is slightly corrupted. In this application scenario the classical Duplication with Comparison (DWC) scheme, that rejects images (and requires re-executions) when the two replicas' outputs differ in a per-pixel comparison, may be over-conservative. In this paper, we propose a novel lightweight fault tolerant scheme specifically tailored for image processing applications. The proposed scheme enhances the state-of-the-art by: i) improving the DWC scheme by replacing one of the two exact replicas with an approximated counterpart, and ii) allowing to distinguish between usable and unusable images instead of corrupted and uncorrupted ones by means of a Convolutional Neural Network-based checker.To tune the proposed scheme we introduce a specific design methodology that optimizes both execution time and fault detection capability of the hardened system. We report the results of the application of the proposed approach on two case studies; our proposal achieves an average execution time reduction larger than 30% w.r.t. the DWC with re-execution, and less than 4% misclassified unusable images.

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Lightweight Fault Detection and Management for Image Restoration

Cited by 2 publications

References 14 publications

Artificial Neural Networks for Space and Safety-Critical Applications: Reliability Issues and Potential Solutions

Artificial Neural Networks for Space and Safety-Critical Applications: Reliability Issues and Potential Solutions

Approximation-based Fault Tolerance in Image Processing Applications

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