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Dynamic dataflow models of computation have become widely used through their adoption to popular programming frameworks such as TensorFlow and GNU Radio. Although dynamic dataflow models offer more programming freedom, they lack analyzability compared to their static counterparts (such as synchronous dataflow). In this paper we advocate the use of a boundedly dynamic dataflow model of computation, VR-PRUNE, that remains analyzable but still offers more programming freedom than a fully static dataflow model.

In recent years, the number and variety of heterogeneous multiprocessor system-on-chip MPSoCs, such as for instance Zynq platforms, has sensibly increased. However, today all design flow solutions capable of programming the different components of such platforms require to the designer either to modify the software or hardware based designs to obtain higher performance implementations. Thus, the developer needs to either rewrite functional blocks in HDL or to use high-level synthesis of C-like sequential languages with platform locked extensions.