Buch, Englisch, 131 Seiten, Format (B × H): 168 mm x 240 mm, Gewicht: 261 g
Reihe: Synthesis Lectures on Engineering, Science, and Technology
Buch, Englisch, 131 Seiten, Format (B × H): 168 mm x 240 mm, Gewicht: 261 g
Reihe: Synthesis Lectures on Engineering, Science, and Technology
ISBN: 978-3-031-18601-1
Verlag: Springer International Publishing
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Bauelemente, Schaltkreise
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Ambient Intelligence, RFID, Internet der Dinge
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
- Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
Weitere Infos & Material
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.