LAUSR

Energy optimization is one of the most critical objectives for the synthesis of multiprocessor system-on-chip (MPSoC). Besides, to ensure a long processor lifetime and to maintain a safe chip temperature are also important for multiprocessor manufactures under deep submicrometer process technologies. This paper presents a mixed integer linear programming (MILP) model to determine the mapping and scheduling of real-time applications onto embedded MPSoC platforms, such that the total energy consumption is minimized with the lifetime reliability constraint and the temperature threshold constraint satisfied. We develop a lightweight temperature model that can be integrated in the MILP model to predict the chip temperature accurately and efficiently. By exploiting the dynamic voltage and frequency scaling capability of modern processors, processor voltage/frequency assignment is also considered in our MILP model. Extensive performance evaluations on synthetic and real-world applications demonstrate the effectiveness of the proposed approach. Our MILP model achieves an average reduction of 19.09% and 28.53% total energy in comparison with two state-of-the-art techniques on the basis of guaranteeing the safe chip temperature and system lifetime reliability.