Quasi-static scheduling of data flow graphs in the presence of limited channel capacities

Signal processing algorithms as can be found in multimedia applications are often modeled by dynamic Data Flow Graphs (DFGs), especially when targeting heterogeneous multicore platforms. However, there is often a mismatch between the fine granularity of the application and the coarse granularity of the platform. Tailoring the granularity of the DFG to a given platform by employing Quasi-Static Schedules (QSSs) promises performance gains by reducing dynamic scheduling overhead and enabling optimizations targeting groups of actors instead of individual actors in isolation. Unfortunately, all approaches known from literature to compute QSSs implicitly assume DFGs with unbounded First In First Out (FIFO) channels. In contrast, mappings of DFGs to multi-core platforms must adhere to FIFO channels with limited capacities. In this paper, we present a novel FIFO channel capacity adjustment algorithm that enables QSSs to DFGs with limited channel capacities, thus, extending the scope of QSS refinements to general multi-core targets.