Dependability Challenges in the Model-Driven Engineering of Automotive Systems

Amarnath, Rakshith; Munk, Peter; Thaden, Eike; Nordmann, Arne; Burton, Simon

doi:10.1109/issrew.2016.15

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…Training determines the model's parameters, while inference uses them to make predictions. In contrast to the training procedure, which is typically executed on distributed systems, the inference is usually executed on a single device [29] (e.g., a mobile, edge, and IoT device) where latency [55], as well as energy constraints, may become more serious. So, we require huge inference computation in a large DNN, which leads to higher latency and energy consumption.…”

Section: Refmentioning

confidence: 99%

“…Resilience is an important dependability feature for a safetycritical system operating in an open context, i.e., for which a definitive characteristic of the desired output in response to all possible inputs is impossible due to the application and the environment's complexity. We face this open-context dependability challenge in upcoming technologies like autonomous vehicles [55]. DNNs are enabling such technologies due to their ability to deal with open-context situations.…”

Section: Aging-aware Resiliencementioning

confidence: 99%

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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: Refmentioning

confidence: 99%

Section: Aging-aware Resiliencementioning

confidence: 99%