Energy-optimal software partitioning in heterogeneous multiprocessor embedded systems

Embedded systems with heterogeneous processors extend the energy/timing trade-off flexibility and provide the opportunity to fine tune resource utilization for particular applications. In this paper, we present a resource model that considers the time and energy costs of run-time mode switching, which considerably improves the accuracy of existing models. Given an application, the software partitioning problem then becomes an optimization over energy cost given deadline constraints, which can be formulate as an integer linear programming (ILP) problem. We apply the resource modeling and software partitioning techniques to a multi- module embedded sensing device, the mPlatform, and present a case study of configuring the platform for a real-time sound source localization application on a stack of MSP430 and ARM7 processor based sensing and processing boards.