Energy Optimization with Worst-Case Deadline Guarantee for Pipelined Multiprocessor Systems

Pipelined computing is a promising paradigm for embedded system design. Designing the scheduling policy for a pipelined system is however more involved. In this paper, we study the problem of the energy minimization for coarse-grained pipelined systems under hard real-time constraints and propose a method based on an inverse use of the pay-burst-only-once principle. We formulate the problem by means of the resource demands of individual pipeline stages and solve it by quadratic programming. Our approach is scalable w.r.t the number of the pipeline stages. Simulation results using real-life applications as well as commercialized processors are presented to demonstrate the effectiveness of our method.