Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices
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Beschreibung
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.
Describes a virtual platform framework (i.e., SOFIA) to conduct soft error reliability assessment of CNN software Uses novel fault injection techniques to assess the impact of CNN models running in resource-constrained devices Discusses relative performance, memory utilization, and soft error reliability trade-offs
Autor*in
Geancarlo Abich
Themen in »Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices«
software reliability soft error analysis Fault Injection Machine Learning Applied to Soft Error Assessment resource-constrained IoT