Enabling adaptation in embedded and real-time systems

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Real-time and embedded systems work under resource constraints to perform computational tasks and satisfy quality of service requirements. Whereas temporal requirements, in the form of task deadlines, have been central to the modelling and analysis of real-time systems, the space of system-level optimizations for such systems is rich. It is possible to trade some measures of service quality (e.g., accuracy of a solution) to satisfy other constraints. Two constraints that we propose to tackle are energy consumption and reliability. Both these factors have, historically, been important for computing systems and their significance is growing. Energy consumption is important for two reasons: (i) many embedded computing systems are battery-powered and need to utilize stored energy efficiently, and (ii) even when systems are not battery-operated, concerns about energy efficiency are important for various reasons including a potential shortfall in traditional energy sources. Reliability concerns are motivated by the shrinking size of CMOS transistors. This technological advance has increased the number of computational cores that can be etched onto one silicon die thereby increasing computational power. The increase in computational power is attractive for real-time and embedded applications as well as personal computers and data centres. On the flip side, smaller transistors are more vulnerable to faults. We believe that software developers should be able to specify not just a correct version of a program but approximate versions as well. Approximate versions would yield energy savings and shorter execution times. The compromise would be on the accuracy of the data computed by the approximate versions. Such approximations would then enable a tradeoff between accuracy, energy efficiency and reliability. To reduce energy consumption or to support recovery from faults within timing constraints, an approximate version of the program could be executed. We propose a research program for studying the value of such approximations and establishing a framework that would enable systems to adapt to energy and reliability constraints by sacrificing correctness within tolerable limits.