Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices (Synthesis Lectures on Engineering, Science, and Technology) （2023）

Abich, Geancarlo/ Ost, Luciano/ Reis, Ricardo

Springer International Publishing AG（2023/01発売）

• 製本 Hardcover:ハードカバー版／ページ数 131 p.
• 言語 ENG
• 商品コード 9783031185984
• DDC分類 004

Full Description

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.

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.