Online hardware/software partitioning in networked embedded systems

Today's embedded systems are typically distributed and more often confronted with time-varying demands. Existing methodologies that optimize the partitioning of computational tasks to hardware (HW) and software (SW) at compile-time become obsolete or inefficient in this context as the optimal use of existing resources cannot be foreseen. Here, we investigate a discrete iterative algorithm that balances the load of a HW/SW partition online: once there are changing computational demands, the system will dynamically assign tasks to reconfigurable HW or SW resources and migrates tasks to other nodes if necessary. For this purpose an Evolutionary Algorithm combined with a discrete version of a diffusion algorithm is presented. Concerning the diffusion algorithm, we will show theoretically and by experiment that our version is run-time optimal in a linear number of steps.