A Quasi-Static Approach to Minimizing Energy Consumption in Real-Time Systems under Reward Constraints

In some real-time applications, it is desirable to trade off precision for timeliness. For such systems, considered typically under the imprecise computation model, a function assigns reward to the application depending on the amount of computation allotted to it. Also, many such applications run on battery-powered devices where the energy consumption is of utmost importance. We address in this paper the problem of energy minimization for imprecise-computation systems that have reward and time constraints. We propose a quasi-static (QS) approach that exploits, with low on-line overhead, the dynamic slack that arises from variations in the actual number of execution cycles: first, at design-time, a set of solutions are computed and stored (off-line phase); second, the selection among the precomputed assignments is left for run-time, based on actual values of time and reward (on-line phase)