Communicating sequential processes
Communicating sequential processes
ACM Computing Surveys (CSUR)
Bounded scheduling of process networks
Bounded scheduling of process networks
Compaan: deriving process networks from Matlab for embedded signal processing architectures
CODES '00 Proceedings of the eighth international workshop on Hardware/software codesign
Communications of the ACM
Communicating sequential processes
Communications of the ACM
Dependence Analysis for Supercomputing
Dependence Analysis for Supercomputing
Algorithmic transformation techniques for efficient exploration of alternative application instances
Proceedings of the tenth international symposium on Hardware/software codesign
An Approach for Quantitative Analysis of Application-Specific Dataflow Architectures
ASAP '97 Proceedings of the IEEE International Conference on Application-Specific Systems, Architectures and Processors
Scheduling dynamic dataflow graphs with bounded memory using the token flow model
Scheduling dynamic dataflow graphs with bounded memory using the token flow model
Heterogeneous Design in Functional DIF
SAMOS '08 Proceedings of the 8th international workshop on Embedded Computer Systems: Architectures, Modeling, and Simulation
Streamflow Programming Model for Data Streaming in Scientific Workflows
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Exploiting Statically Schedulable Regions in Dataflow Programs
Journal of Signal Processing Systems
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
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Modeling applications and architectures at various levels of abstraction is becoming more and more an accepted approach in embedded system design. When looking at the modeling of applications in the domain of video, audio, and graphics applications, we notice that they exhibit a high degree of task parallelism and operate on streams of data. Models that we can use to specify such stream-based applications on a high level of abstraction are the dataflow models and process network models. Each of these models has its own merits. Therefore, an alternative approach is to introduce a model of computation that combines the semantics of both models of computation. In this article, we introduce such a model of computation, which we call the Stream-Based Functions (SBF) model of computation and show an example. Furthermore, we discuss the composition and decomposition of SBF objects and put the SBF model of computation in the context of relevant dataflow models and process network models.