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

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

af Geancarlo Abich

indgår i Synthesis Lectures on Engineering, Science, and Technology serien

Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices af Geancarlo Abich
Bag om Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices

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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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783031186011
  • Indbinding:
  • Paperback
  • Sideantal:
  • 148
  • Udgivet:
  • 3. januar 2024
  • Udgave:
  • 24001
  • Størrelse:
  • 168x9x240 mm.
  • Vægt:
  • 261 g.
  • 8-11 hverdage.
  • 10. december 2024
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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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