An intelligent approach for optimizing Energy consumption and Schedule length of Embedded multiprocessors 1

Abstract

In contemporary and future embedded as well as high-performance microprocessors, power consumption is one of the most important design considerations. Because in current technologies, the dynamic power consumption dominates the static power consumption, voltage scaling is an effective technique to reduce the power consumption. In multiprocessor systems, an efficient scheduling of sequential and parallel tasks onto the processors is known to be NP- Hard problem. In this paper, the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint on homogeneous and heterogeneous multiprocessor computers through independent sequential and parallel tasks are proposed. These problems emphasize the tradeoff between power and performance and are defined such that the power-performance product is optimized by fixing one factor and minimizing the other and vice versa. The performances of the proposed algorithm with optimal solutions are validated using Discrete Particle Swarm Optimization (DPSO). The proposed algorithms achieve 47.5% of power savings and 45.5% of energy saving with 23.5% increased schedule length when the processors operate its maximum frequency.