Trading off power and fault-tolerance in real-time embedded systems

Reliability and fault-tolerance are essential requirements of critical, autonomous computing systems. In this paper, we propose a methodology to quantify, and maximize, the reliability of computation in the presence of transient errors when considering the mapping of real-time tasks on an homogeneous multiprocessor system with voltage and frequency scaling capabilities. As the likelihood of transient errors due to radiation is environment- and component-specific, we use machine learning to estimate the actual fault-rate of the system. Furthermore, we leverage probability theory to define a trade-off between power consumption and fault-tolerance. If a processing element fails, our methodology is able to re-map the application, establishing whether the real-time requirements will still be met, and how reliable the new, impaired system will be. Results show that the proposed methodology is able to adjust mapping and operating frequencies in order to maintain a fixed level of reliability for different fault-rates.