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A Comparison of Synchronous and CycloStatic Dataflow
, 1995
"... We compare synchronous dataflow (SDF) and cyclostatic dataflow (CSDF), which are each special cases of a model of computation we call dataflow process networks. In SDF, actors have static firing rules: they consume and produce a fixed number of data tokens in each firing. This model is well suited ..."
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Cited by 27 (0 self)
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We compare synchronous dataflow (SDF) and cyclostatic dataflow (CSDF), which are each special cases of a model of computation we call dataflow process networks. In SDF, actors have static firing rules: they consume and produce a fixed number of data tokens in each firing. This model is well suited
ThroughputBuffering TradeOff Exploration for CycloStatic and Synchronous Dataflow Graphs
"... Multimedia applications usually have throughput constraints. An implementation must meet these constraints, while it minimizes resource usage and energy consumption. The compute intensive kernels of these applications are often specified as CycloStatic or Synchronous Dataflow Graphs. Communication ..."
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Cited by 34 (9 self)
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Multimedia applications usually have throughput constraints. An implementation must meet these constraints, while it minimizes resource usage and energy consumption. The compute intensive kernels of these applications are often specified as CycloStatic or Synchronous Dataflow Graphs. Communication
The synchronous dataflow programming language LUSTRE
 Proceedings of the IEEE
, 1991
"... This paper describes the language Lustre, which is a dataflow synchronous language, designed for programming reactive systems  such as automatic control and monitoring systems  as well as for describing hardware. The dataflow aspect of Lustre makes it very close to usual description tools in t ..."
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Cited by 647 (53 self)
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This paper describes the language Lustre, which is a dataflow synchronous language, designed for programming reactive systems  such as automatic control and monitoring systems  as well as for describing hardware. The dataflow aspect of Lustre makes it very close to usual description tools
Mapping Parameterized Cyclostatic Dataflow Graphs onto Configurable Hardware
 JOURNAL OF SIGNAL PROCESSING SYSTEMS, 66(3):285301, 2012
, 2012
"... In recent years, parameterized dataflow has evolved as a useful framework for modeling synchronous and cyclostatic graphs in which arbitrary parameters can be changed dynamically. Parameterized dataflow has proven to have significant expressive power for managing dynamics of DSP applications in imp ..."
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Cited by 1 (1 self)
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In recent years, parameterized dataflow has evolved as a useful framework for modeling synchronous and cyclostatic graphs in which arbitrary parameters can be changed dynamically. Parameterized dataflow has proven to have significant expressive power for managing dynamics of DSP applications
Affine nested loop programs and their binary cyclostatic dataflow counterparts
 in Proceedings of the International Conference on Application Specific Systems, Architectures, and Processors, Steamboat
, 2006
"... Parameterized static affine nested loop programs can be automatically converted to inputoutput equivalent Kahn Process Network specifications. These networks turn out to be close relatives of parameterized cyclostatic dataflow graphs. Token production and consumption can be cyclic with a finite nu ..."
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Cited by 10 (4 self)
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Parameterized static affine nested loop programs can be automatically converted to inputoutput equivalent Kahn Process Network specifications. These networks turn out to be close relatives of parameterized cyclostatic dataflow graphs. Token production and consumption can be cyclic with a finite
Dataflow Process Networks
 Proceedings of the IEEE
, 1995
"... We review a model of computation used in industrial practice in signal processing software environments and experimentally in other contexts. We give this model the name "dataflow process networks," and study its formal properties as well as its utility as a basis for programming language ..."
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Cited by 324 (33 self)
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, the considerable overhead of context switching incurred in most implementations of Kahn process networks is avoided. We relate dataflow process networks to other dataflow models, including those used in dataflow machines, such as static dataflow and the taggedtoken model. We also relate dataflow process networks
Community detection in graphs
, 2009
"... The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of th ..."
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Cited by 801 (1 self)
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The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices
Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing
 IEEE TRANSACTIONS ON COMPUTERS
, 1987
"... Large grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or costsensitive applications. In some situations, designers are not willing to squander computing resources for the sake of pro ..."
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Cited by 592 (37 self)
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not be done at runtime, but can be done at compile time (statically), so the runtime overhead evaporates. The sample rates can all be different, which is not true of most current datadriven digital signal processing programming methodologies. Synchronous data flow is closely related to computation graphs, a
Factor Graphs and the SumProduct Algorithm
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple c ..."
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Cited by 1787 (72 self)
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A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple
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