SHeD: A Framework for Automatic Software Synthesis of Heterogeneous Dataflow Process Networks

A dataflow process network (DPN) is a system of concurrent processes which communicate with each other through statically determined and buffered point-to-point connections. While the general model of computation (MoC) does not impose further restrictions, many different subclasses of DPNs have been considered over time like Kahn process networks, cyclo-static networks and synchronous dataflow networks. These classes differ in the kinds of behaviors of the processes that are precisely described based on how each process is triggered for an execution, and based on how each execution of a process consumes/produces data. A heterogeneous combination of particular kinds of processes can be effectively used to model different components of a system with different kinds of MoCs. Such a composition of dataflow processes within a network is termed as heterogeneous DPN. There are design tools for modeling like Ptolemy and FERAL that support different MoCs including particular classes of DPNs by the use of so-called directors. However, design tools for synthesis are usually restricted to the weakest classes of DPNs, i.e., cyclo-static and synchronous DPNs. In this paper, we present an extendable model-based design framework called SHeD for automatic -software synthesis of heterogeneous -DPNs. SHeD supports different kinds of DPN processes and therefore also different kinds of MoCs. To this end, SHeD proposes a general DPN model that is used with specific definitions and constraints to formulate the precise classes of DPNs. Also, it provides a tool chain, including different specialized code generators for specific MoCs, and a runtime system that finally maps models using a combination of different MoCs on the target hardware. We demonstrate the effective use of SHeD by a case study of a distributed automotive research platform.