Runtime adaptive allocation of dynamically mixed tasks on a heterogeneous MPSoC platform

Multiprocessor System-on-Chip platforms are typically used for co-hosting multiple tasks, which may start and stop execution independently at time instants unknown at design time. In such systems, the runtime resource manager is responsible for allocating adequate and appropriate resources to each task. We identify a key issue in existing work that the resource management algorithms consider the problem only at task-level, i.e. the optimization is performed for each individual task upon activation. However, it can be shown that such strategies are suboptimal from the system point of view. In contrast, we propose in this paper a new task allocation flow that considers the system-level resource management. Comparing with traditional techniques, significant performance improvement (up to 29.5%) is observed during evaluation using a standard benchmark set. In addition, the proposed task allocator features runtime self-adaptability with respect to changes in hardware and/or applications.