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

Rafique, Omair; Schneider, Klaus

doi:10.1109/dsd51259.2020.00012

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Dynamic dataflow MoCs, of which dataflow process networks (DPN) [21] is one of the most well-known examples, allow port token rates to change arbitrarily at application run time, and therefore possibilities for graph consistency verification at design time are very limited. In the past decade, dynamic dataflow around the CAL language [22] and its subvariant RVC-CAL [23] has triggered the development of several design frameworks such as Tÿcho [24], Orcc [25] and SHeD [26].…”

Section: Introductionmentioning

confidence: 99%

VR-PRUNE: Decidable Variable-Rate Dataflow for Signal Processing Systems

Boutellier

et al. 2022

IEEE Trans. Signal Process.

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The dataflow concept has been successfully used for modeling and synthesizing signal processing applications since decades, and recently, dataflow has also been discovered to match the computation model of machine learning applications, leading to extremely successful dataflow based application design frameworks. One of the most attractive features of dataflow, especially for signal processing, is related to its formal nature: when properly defined, a dataflow-based application model can be analytically verified for correctness at the stage of application design. This paper proposes VR-PRUNE, a novel dataflow model of computation that is aimed for design of high-performance signal processing software, together with runtime support that allows efficient application deployment to heterogeneous GPU-equipped platforms. Compared to prior work, VR-PRUNE features variable token rate processing, which enables designing adaptive signal processing applications, and implementing solutions that, e.g., allow trading-off between power consumption and filtering bandwidth at runtime. The paper presents the formal concepts of VR-PRUNE, as well as four application examples from domains related to signal processing, accompanied with quantitative results, which show that using VR-PRUNE enables, for example, application power-performance scaling, and on the other hand describing adaptive application behavior with 59% fewer dataflow graph components compared to previous work.

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Section: Introductionmentioning

confidence: 99%

VR-PRUNE: Decidable Variable-Rate Dataflow for Signal Processing Systems

Boutellier

et al. 2022

IEEE Trans. Signal Process.

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Synthesis of Parallel Software from Heterogeneous Dataflow Models

Rafique

Schneider

2022

SN COMPUT. SCI.

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Dataflow process networks (DPNs) are intrinsically data-driven, i.e., node actions are not synchronized among each other and may fire whenever sufficient input operands arrived at a node. While the general model of computation (MoC) of DPNs does not impose further restrictions, many different subclasses of DPNs representing different dataflow MoCs have been considered over time. These classes mainly differ in the kinds of behaviors of the processes. A DPN may be heterogeneous in that different processes in the network belong to different classes of DPNs. A heterogeneous DPN can therefore be effectively used to model and to implement different components of a system with different kinds of processes and, therefore, different dataflow MoCs. This paper presents a model-based design based on different dataflow MoCs including their heterogeneous combinations. In particular, it covers the automatic software synthesis of systems from DPN models. The main objective is to validate, evaluate and compare the artifacts exhibited by different dataflow MoCs at the implementation level of systems under the supervision of a common design tool. Moreover, this work also offers an efficient synthesis method that targets and exploits heterogeneity in DPNs by generating implementations based on the kinds of behaviors of the processes. The proposed synthesis method provides a tool chain including different specialized code generators for specific dataflow MoCs, and a runtime system that finally maps models using a combination of different dataflow MoCs on cross-vendor target hardware.

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

Cited by 2 publications

References 14 publications

VR-PRUNE: Decidable Variable-Rate Dataflow for Signal Processing Systems

VR-PRUNE: Decidable Variable-Rate Dataflow for Signal Processing Systems

Synthesis of Parallel Software from Heterogeneous Dataflow Models

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