GPU Devices for Safety-Critical Systems: A Survey

Pérez-Cerrolaza, Jon; Abella, Jaume; Kosmidis, Leonidas; Calderon, Alejandro J.; Cazorla, Francisco J.; Flores, José Luis Martínez

doi:10.1145/3549526

Cited by 20 publications

(18 citation statements)

References 171 publications

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“…The article [16] reviews research contributions to address random hardware failures, systematic failures, and execution independence in GPU devices. It discusses several challenges of safety certification in complex, parallel, and compute-intensive software functions with different safety-criticality levels on shared GPU devices.…”

Section: A Papers With Similar Backgroundmentioning

confidence: 99%

Dependable DNN Accelerator for Safety-Critical Systems: A Review on the Aging Perspective

Moghaddasi,

Gorgin,

Lee

2023

IEEE Access

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Nowadays, artificial intelligence (AI) and deep learning (DL) progressively adapt to various spheres of our lives. These disciplines contain safety-critical applications such as autonomous driving with a high risk of human injury in the case of malfunction, requiring a high promise of dependability. Even the dependability becomes more crucial as shrinking CMOS technology feature size worsens the resilience concerns due to factors like aging. This paper addresses the overarching dependability issue of advanced deep neural networks (DNN) accelerators from the aging perspective. Especially, a comprehensive survey and taxonomy of techniques used to evaluate and mitigate aging effects are introduced. We cover different aging effects like permanent faults, timing errors, and lifetime issues. We review research by the layer-wise approach and categorize several resilience classes to bring out major features. The concluding part of this review highlights the questions answered and several future research directions. This study is expected to benefit researchers in different areas of DNN deployment, especially the dependability of this emergent paradigm.

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Section: A Papers With Similar Backgroundmentioning

confidence: 99%

Dependable DNN Accelerator for Safety-Critical Systems: A Review on the Aging Perspective

Moghaddasi,

Gorgin,

Lee

2023

IEEE Access

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“…The use of advanced highperformance Commercial Off-The-Shelf (COTS) platforms is known to hamper the analyzability of a system [24], and to cause significant performance issues due to contention accessing hardware shared resources such as memories and caches, for which some limited solutions have been proposed recently [25], [26]. However, to our knowledge, no COTS hardware exists that allows excluding all sources of interference [27]. Some solutions have been proposed in different domains to deal with timing interference at software-level [28] but none of them has proven to be the one-fits-all solution, even for a single domain.…”

Section: Addressing Performance and Platform-level Concerns For Safel...mentioning

confidence: 99%

SAFEXPLAIN: Safe and Explainable Critical Embedded Systems Based on AI

Abella,

Perez,

Englund

et al. 2023

2023 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Deep Learning (DL) techniques are at the heart of most future advanced software functions in Critical Autonomous AI-based Systems (CAIS), where they also represent a major competitive factor. Hence, the economic success of CAIS industries (e.g., automotive, space, railway) depends on their ability to design, implement, qualify, and certify DL-based software products under bounded effort/cost. However, there is a fundamental gap between Functional Safety (FUSA) requirements on CAIS and the nature of DL solutions. This gap stems from the development process of DL libraries and affects high-level safety concepts such as (1) explainability and traceability, (2) suitability for varying safety requirements, (3) FUSA-compliant implementations, and (4) real-time constraints. As a matter of fact, the datadependent and stochastic nature of DL algorithms clashes with current FUSA practice, which instead builds on deterministic, verifiable, and pass/fail test-based software. The SAFEXPLAIN project tackles these challenges and targets by providing a flexible approach to allow the certification -hence adoption -of DL-based solutions in CAIS building on: (1) DL solutions that provide end-to-end traceability, with specific approaches to explain whether predictions can be trusted and strategies to reach (and prove) correct operation, in accordance to certification standards; (2) alternative and increasingly sophisticated design safety patterns for DL with varying criticality and fault tolerance requirements; (3) DL library implementations that adhere to safety requirements; and (4) computing platform configurations, to regain determinism, and probabilistic timing analyses, to handle the remaining nondeterminism.

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“…Integrating GPUs into Real-Time Systems requires reasoning over the timing behavior of GPU kernels. The increasing use of GPUs [5] in real-time embedded systems provides severe challenges in developing methods to find the Worst-Case Execution Time (WCET) of the GPU kernel.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Machine Learning Techniques for Worst-Case Execution Time Estimation on GPU Architectures

Kumar,

Ranjbar,

Kumar

2024

IEEE Access

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The massive parallelism provided by Graphics Processing Units (GPUs) to accelerate computeintensive tasks makes it preferable for Real-Time Systems such as autonomous vehicles. Such systems require the execution of heavy Machine Learning (ML) and Computer Vision applications because of the computing power of GPUs. However, such systems need a guarantee of timing predictability. It means the Worst-Case Execution Time (WCET) of the application is estimated tightly and safely to schedule each application before its deadline to avoid catastrophic consequences. As more applications use GPUs, running many applications simultaneously on the same GPU becomes necessary. To provide predictable performance while the application is running in parallel, it must be WCET-aware, which GPUs do not fully support in a multitasking environment. Nvidia recently added a feature called the Multi-Process Service. It allows the different applications to run simultaneously in the same CUDA context by partitioning the compute resources of the GPU. Using this feature, we can measure the interference from co-running GPU applications to estimate WCET. In this paper, we propose a novel technique to estimate the WCET of the GPU kernel using an ML approach. Our approach is based on the application's source, and the model is trained based on the large data set. The approach is flexible and can be applied to different GPU-sharing mechanisms. We allow the victim and enemy kernel of the GPU to execute in parallel to get the maximum interference from the enemy to estimate the WCET of the victim kernel. Enemy kernels are chosen to cause a higher slowdown by acquiring the resources of the victim kernel. We compare our implementation with state-ofthe-art approaches to show its effectiveness. Our ML approach reduces the time by 99% in most cases because inferences take only seconds to predict WCET, and the resource consumption required to estimate WCET compared to traditional approaches is minimal because we don't need to execute the application on GPU for hours. Although our approach does not offer safety guarantees because of its empirical nature, we observed that predicted WCETs are always higher than any observed execution times for all benchmarks, and the maximum overestimation factor observed is 11x.

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GPU Devices for Safety-Critical Systems: A Survey

Cited by 20 publications

References 171 publications

Dependable DNN Accelerator for Safety-Critical Systems: A Review on the Aging Perspective

Dependable DNN Accelerator for Safety-Critical Systems: A Review on the Aging Perspective

SAFEXPLAIN: Safe and Explainable Critical Embedded Systems Based on AI

Utilizing Machine Learning Techniques for Worst-Case Execution Time Estimation on GPU Architectures

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