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Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices

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Abstract

The soft error assessment and mitigation literature are abundant, requiring a taxonomy to classify the different approaches. This book considers the definitions from Avižienis et al. [1] for fault, error, and failure. A fault is an event that may cause the internal state of the system to change, e.g., a radiation particle strike. When a fault affects the system’s internal state, it becomes an error. If the error causes a deviation of at least one of the system’s external states, then it is considered as a failure. To achieve compliance with safety and reliability standard requirements, it is of utmost importance to provide systems with appropriate mechanisms to tackle systematic, SEU, or SET faults, also known as soft errors. In this regard, this Chapter presents a literature review of the works related to this Book’s contributions to the soft error reliability assessment of ML models executing on resource-constrained IoT systems. First, Sect. 3.1 presents a review of fault injector frameworks implemented on the top of VPs. Next, Sect. 3.2 discusses some related works on soft error reliability assessment of ML models in different scopes. Finally, we distinguish this Book from the works found in the literature (Sects. 3.2.2 and 3.1.1).

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Notes

  1. 1.

    The adopted RTL model must allow to access the register file and memory addressing.

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Authors and Affiliations

  1. University of Santa Cruz do Sul, Santa Cruz do Sul, Rio Grande do Sul, Brazil

    Geancarlo Abich

  2. Loughborough University, Loughborough, UK

    Luciano Ost

  3. Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil

    Ricardo Reis

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  1. Geancarlo Abich
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Abich, G., Ost, L., Reis, R. (2023). Related Works. In: Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices. Synthesis Lectures on Engineering, Science, and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-18599-1_3

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