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Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices [electronic resource] / by Geancarlo Abich, Luciano Ost, Ricardo Reis.

By: Abich, Geancarlo [author.].
Contributor(s): Ost, Luciano [author.] | Reis, Ricardo [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Engineering, Science, and Technology: Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2023Edition: 1st ed. 2023.Description: XV, 131 p. 47 illus., 44 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031185991.Subject(s): Electronic circuits | Internet of things | Electronic circuit design | Cooperating objects (Computer systems) | Electronic Circuits and Systems | Internet of Things | Electronics Design and Verification | Cyber-Physical SystemsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.3815 Online resources: Click here to access online
Contents:
Introduction -- Background in ML Models and Radiation Effects -- Related Works -- Soft Error Assessment Methodology -- Early Soft Error Consistency Assessment -- Soft Error Reliability Assessment of ML Inference Models executing on resource-constrained IoT edge devices -- Conclusions and Future Work.
In: Springer Nature eBookSummary: This book describes an extensive and consistent soft error assessment of convolutional neural network (CNN) models from different domains through more than 14.8 million fault injections, considering different precision bit-width configurations, optimization parameters, and processor models. The authors also evaluate the relative performance, memory utilization, and soft error reliability trade-offs analysis of different CNN models considering a compiler-based technique w.r.t. traditional redundancy approaches.
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Introduction -- Background in ML Models and Radiation Effects -- Related Works -- Soft Error Assessment Methodology -- Early Soft Error Consistency Assessment -- Soft Error Reliability Assessment of ML Inference Models executing on resource-constrained IoT edge devices -- Conclusions and Future Work.

This book describes an extensive and consistent soft error assessment of convolutional neural network (CNN) models from different domains through more than 14.8 million fault injections, considering different precision bit-width configurations, optimization parameters, and processor models. The authors also evaluate the relative performance, memory utilization, and soft error reliability trade-offs analysis of different CNN models considering a compiler-based technique w.r.t. traditional redundancy approaches.

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