TY - GEN
T1 - Learning-Oriented Reliability Improvement of Computing Systems from Transistor to Application Level
AU - Ranjbar, Behnaz
AU - Klemme, Florian
AU - Genssler, Paul R.
AU - Amrouch, Hussam
AU - Jung, Jinhyo
AU - Dave, Shail
AU - So, Hwisoo
AU - Lee, Kyongwoo
AU - Shrivastava, Aviral
AU - Lin, Ji Yung
AU - Weckx, Pieter
AU - Mishra, Subrat
AU - Catthoor, Francky
AU - Biswas, Dwaipayan
AU - Kumar, Akash
N1 - Publisher Copyright:
© 2023 EDAA.
PY - 2023
Y1 - 2023
N2 - Due to technology scaling in modern computing platforms, the safety and reliability issues have increased tremendously, which often accelerate aging, lead to permanent faults, and cause unreliable execution of applications. Failure in some computing systems like avionics may cause catastrophic consequences. Therefore, managing reliability under all circumstances of stress and environmental changes is crucial in all abstraction layers, from application to transistor levels. Machine learning techniques are recently being employed for dynamic reliability estimation and optimization. They can adapt to varying workloads and system conditions. This paper presents reliability improvement approaches from multiple perspectives-from transistor-level to application-level-and discusses their effectiveness and limitations as well as open challenges.
AB - Due to technology scaling in modern computing platforms, the safety and reliability issues have increased tremendously, which often accelerate aging, lead to permanent faults, and cause unreliable execution of applications. Failure in some computing systems like avionics may cause catastrophic consequences. Therefore, managing reliability under all circumstances of stress and environmental changes is crucial in all abstraction layers, from application to transistor levels. Machine learning techniques are recently being employed for dynamic reliability estimation and optimization. They can adapt to varying workloads and system conditions. This paper presents reliability improvement approaches from multiple perspectives-from transistor-level to application-level-and discusses their effectiveness and limitations as well as open challenges.
KW - Aging
KW - Cross-layer reliability
KW - Device and circuit reliability
KW - Dynamic reliability estimation
KW - Error mitigation
KW - Machine learning for systems
KW - Task scheduling
KW - Timing reliability
UR - https://www.scopus.com/pages/publications/85162615171
UR - https://www.scopus.com/pages/publications/85162615171#tab=citedBy
U2 - 10.23919/DATE56975.2023.10137182
DO - 10.23919/DATE56975.2023.10137182
M3 - Conference contribution
AN - SCOPUS:85162615171
T3 - Proceedings -Design, Automation and Test in Europe, DATE
BT - 2023 Design, Automation and Test in Europe Conference and Exhibition, DATE 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Design, Automation and Test in Europe Conference and Exhibition, DATE 2023
Y2 - 17 April 2023 through 19 April 2023
ER -