Optimal IC Task Mapping to Maximize QoS on Heterogeneous Multicore Systems

Heterogeneous multicore architectures have become one of the most widely used hardware platforms for embedded systems, where time, energy, and system QoS are the major concerns. The Imprecise Computation (IC) model splits a task into mandatory and optional parts, allowing the trade-off of the above issues. However, existing approaches, to maximize system QoS (Quality-of-Service) under time or energy constraints, use a linear function to model system QoS. Therefore, they become unsuitable for general applications, whose QoS is modeled by a concave function. To deal with this limitation, this brief addresses the Mixed-Integer Non-Linear Programming (MINLP) problem of mapping IC tasks to a set of heterogeneous cores by concurrently deciding which processor executes each task and the number of cycles of optional tasks (i.e., task allocation and scheduling), under real-time and energy supply constraints. Furthermore, as existing solution algorithms either demand high time complexity or only achieve feasible solutions, we propose a novel approach based on problem transformation and dual decomposition that finds an optimal solution while avoiding high computational complexity. Simulation results show that the proposed approach achieves 98% performance of the optimization solver Gurobi, but only with 19.8% of its computation time.