The work presented in this paper addresses minimization of the energy consumption of a system during systemlevel design. The paper focuses on scheduling techniques for architectures containing variable supply voltage processors, running dependent tasks. We introduce our new approach for LowEnergy Scheduling (LEneS) and compare it to two other scheduling methods. LEneS is based on a list-scheduling heuristic with dynamic recalculation of priorities, and assumes a given allocation and assignment of tasks to processors. Our approach minimizes the energy by choosing the best combination of supply voltages for each task running on its processor. The set of experiments we present shows that, using the LEneS approach, we can achieve up to 28% energy savings for the tightest deadlines, and up to 77% energy savings when these deadlines are relaxed by 50%.
This paper describes a new method for modeling and solving different scheduling and resource assignment problems that are common in high-level synthesis (HLS) and system-level synthesis. It addresses assignment of resources for operations and tasks as well as their static, off-line scheduling. Different heterogeneous constraints are considered for these problems. These constraints can be grouped into two classes: problem-specific constraints and design-oriented constraints. They are uniformly modeled, in our approach, by finite domain (FD) constraints and solved using related constrained programming (CP) techniques. This provides a way to improve quality of final solutions. We have developed in Java a constraint solver engine, JaCoP (Java Constraint Programming), to evaluate this approach. This solver and a related framework make it possible to model different resource assignment and scheduling problems, and handle them uniformly. The JaCoP prototype system has been extensively evaluated on a number of HLS and system-level synthesis benchmarks. We have been able to obtain optimal results together with related proofs of optimality for all HLS scheduling benchmarks and for all explored design styles (except one functional pipeline design). Many system-level benchmarks can also be solved optimally. For large randomly generated task graphs, we have used heuristic search methods and obtained results that are 1-3% worse than lower bounds or optimal results. These experiments have proved the feasibility of the presented approach.
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