Passive-Active Flowgraphs for Efficient Modeling and Design of Signal Processing Systems

In dataflow representations for signal processing systems, applications are represented as directed graphs in which vertices represent computations and edges correspond to buffers that store data as it passes among computations. The edges in the dataflow graph are single-input, single-output compone...

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Veröffentlicht in:Journal of signal processing systems Jg. 92; H. 10; S. 1133 - 1151
Hauptverfasser: Lee, Yaesop, Liu, Yanzhou, Desnos, Karol, Barford, Lee, Bhattacharyya, Shuvra S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.10.2020
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ISSN:1939-8018, 1939-8115
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Abstract In dataflow representations for signal processing systems, applications are represented as directed graphs in which vertices represent computations and edges correspond to buffers that store data as it passes among computations. The edges in the dataflow graph are single-input, single-output components that manage data transmission in a first-in, first-out (FIFO) fashion. In this paper, we formulate the vertices and edges into concepts called “active blocks” and “passive blocks”, respectively in the graph representation. Computation in the dataflow graph is represented as “active blocks”, while the concept of dataflow buffers is represented as “passive blocks”. Like dataflow edges, passive blocks are used to store data during the intervals between its production and consumption by actors. However, passive blocks can have multiple inputs and multiple outputs, and can incorporate operations on and rearrangements of the stored data subject to certain constraints. We define a form of flowgraph representation that is based on replacing dataflow edges with the proposed concept of passive blocks. We present a structured design methodology for utilizing this new form of signal processing flowgraph, and demonstrate its application to improving memory management efficiency, and execution time performance.
AbstractList In dataflow representations for signal processing systems, applications are represented as directed graphs in which vertices represent computations and edges correspond to buffers that store data as it passes among computations. The edges in the dataflow graph are single-input, single-output components that manage data transmission in a first-in, first-out (FIFO) fashion. In this paper, we formulate the vertices and edges into concepts called "active blocks" and "passive blocks", respectively in the graph representation. Computation in the dataflow graph is represented as "active blocks", while the concept of dataflow buffers is represented as "passive blocks". Like dataflow edges, passive blocks are used to store data during the intervals between its production and consumption by actors. However, passive blocks can have multiple inputs and multiple outputs, and can incorporate operations on and rearrangements of the stored data subject to certain constraints. We define a form of flowgraph representation that is based on replacing dataflow edges with the proposed concept of passive blocks. We present a structured design methodology for utilizing this new form of signal processing flowgraph, and demonstrate its application to improving memory management efficiency, and execution time performance.
Author Lee, Yaesop
Bhattacharyya, Shuvra S.
Desnos, Karol
Barford, Lee
Liu, Yanzhou
Author_xml – sequence: 1
  givenname: Yaesop
  orcidid: 0000-0002-9293-4155
  surname: Lee
  fullname: Lee, Yaesop
  email: yaesop@umd.edu
  organization: University of Maryland
– sequence: 2
  givenname: Yanzhou
  surname: Liu
  fullname: Liu, Yanzhou
  organization: University of Maryland
– sequence: 3
  givenname: Karol
  surname: Desnos
  fullname: Desnos, Karol
  organization: Univ Rennes, INSA Rennes, CNRS, IETR – UMR
– sequence: 4
  givenname: Lee
  surname: Barford
  fullname: Barford, Lee
  organization: Keysight Laboratories, Keysight Technologies Inc
– sequence: 5
  givenname: Shuvra S.
  surname: Bhattacharyya
  fullname: Bhattacharyya, Shuvra S.
  organization: University of Maryland, INSA/IETR/INRIA
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Issue 10
Keywords Buffer management
Signal processing systems
Model-based design
Dataflow
Language English
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PublicationTitle Journal of signal processing systems
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Li, L., Sapio, A., Wu, J., Liu, Y., Lee, K., Wolf, M., & Bhattacharyya, S.S. (2017). Design and implementation of adaptive signal processing systems using Markov decision processes. In Proceedings of the International Conference on Application Specific Systems, Architectures, and Processors (pp. 170–175). Washington: Seattle.
Liu, Y., Barford, L., & Bhattacharyya, S.S. (2015). Constant-rate clock recovery and jitter measurement on deep memory waveforms using dataflow. In Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, pp. 1590–1595.
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References_xml – reference: BilsenGEngelsMLauwereinsRPeperstraeteJACyclo-static dataflowIEEE Transactions on Signal Processing199644239740810.1109/78.485935
– reference: OhHHaSFractional rate dataflow model and efficient code synthesis for multimedia applicationsACM SIGPLAN Notices200237415110.1145/566225.513834
– reference: Benazouz, M., Marchetti, O., Munier-Kordon, A., & Urard, P. (2010). A new approach for minimizing buffer capacities with throughput constraint for embedded system design. In Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications, pp. 1–8.
– reference: Bhattacharyya, S.S., Deprettere, E., & Leupers, R. (2019) In Takala, J. (Ed.), Handbook of Signal Processing Systems, 3rd edn. Berlin: Springer.
– reference: MurthyPKBhattacharyyaSSBuffer merging — a powerful technique for reducing memory requirements of synchronous dataflow specificationsACM Transactions on Design Automation of Electronic Systems20049221223710.1145/989995.989999
– reference: Buck, J.T., & Lee, E.A. (1993). Scheduling dynamic dataflow graphs using the token flow model. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing.
– reference: Desnos, K., Pelcat, M., Nezan, J.F., & Aridhi, S. (2016). On memory reuse between inputs and outputs of dataflow actors ACM Transactions on Embedded Computing Systems 15(2).
– reference: Reekie, H.J. (1995). Realtime signal processing — dataflow, visual, and functional programming. Ph.D. thesis, University of Technology at Sydney.
– reference: Lin, S., Liu, Y., Lee, K., Li, L., Plishker, W., & Bhattacharyya, S.S. (2017). The DSPCAD framework for modeling and synthesis of signal processing systems. In Ha, S., & Teich, J. (Eds.) handbook of hardware/software codesign, pp. 1–35. Springer.
– reference: Liu, Y., Barford, L., & Bhattacharyya, S.S. (2015). Constant-rate clock recovery and jitter measurement on deep memory waveforms using dataflow. In Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, pp. 1590–1595.
– reference: Tripakis, S., Bui, D., Geilen, M., Rodiers, B., & Lee, E.A. (2013). Compositionality in synchronous data flow: Modular code generation from hierarchical SDF graphs ACM Transactions on Embedded Computing Systems 12(3).
– reference: Liu, Y., Barford, L., & Bhattacharyya, S.S. (2018). Generalized graph connections for dataflow modeling of DSP applications. In Proceedings of the IEEE Workshop on Signal Processing Systems (pp. 275–280). South Africa: Cape Town.
– reference: Bhattacharyya, S.S., & Lee, E.A. (1992). Memory management for synchronous dataflow programs. Tech. Rep. UCB/ERL M92/128, Electronics Research Laboratory University of California at Berkeley.
– reference: Buck, J.T. (1994). Static scheduling and code generation from dynamic dataflow graphs with integer-valued control streams. In Proceedings of the IEEE Asilomar Conference on Signals, Systems, and Computers, pp. 508–513.
– reference: Lesparre, Y., Munier-Kordon, A., & Delosme, J.M. (2016). Evaluation of synchronous dataflow graph mappings onto distributed memory architectures. In Proceedings of the Euromicro Conference on Digital System Design, pp. 146–153.
– reference: SchmogrowRError vector magnitude as a performance measure for advanced modulation formatsIEEE Photonics Technology Letters2012241616310.1109/LPT.2011.2172405
– reference: Stuijk, S., Geilen, M., & Basten, T. (2006). Exploring tradeoffs in buffer requirements and throughput constraints for synchronous dataflow graphs. In Proceedings of the Design Automation Conference.
– reference: BoutellierJErsfolkJLiliusJMattavelliMRoquierGSilvenOActor merging for dataflow process networksIEEE Transactions on Signal Processing2015631024962508334175210.1109/TSP.2015.2411229
– reference: Ha, S., Kim, S., Lee, C., Yi, Y., Kwon, S., & Joo, Y.P. (2007). PeaCE: A hardware-software codesign environment for multimedia embedded systems. ACM Transactions on Design Automation of Electronic Systems 12. Article No. 24.
– reference: Sander, I. (2003). System modeling and design refinement in ForSyDe. Ph.D. thesis, Royal Institute of Technology, Sweden.
– reference: de Oliveira Castro, P., Louise, S., & Barthou, D. (2010). A multidimensional array slicing DSL for stream programming. In Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, pp. 913–918.
– reference: LeeEAMesserschmittDGSynchronous dataflowProceedings of the IEEE19877591235124510.1109/PROC.1987.13876
– reference: Fischaber, S., Woods, R., & McAllister, J. (2007). Soc memory hierarchy derivation from dataflow graphs. In Proceedings of the IEEE Workshop on Signal Processing Systems.
– reference: Li, L., Sapio, A., Wu, J., Liu, Y., Lee, K., Wolf, M., & Bhattacharyya, S.S. (2017). Design and implementation of adaptive signal processing systems using Markov decision processes. In Proceedings of the International Conference on Application Specific Systems, Architectures, and Processors (pp. 170–175). Washington: Seattle.
– reference: Ade, M., Lauwereins, R., & Peperstraete, J. (1997). Data memory minimisation for synchronous data flow graphs emulated on DSP-FPGA targets. In Proceedings of the Design Automation Conference, pp. 64–69.
– reference: EkerJJanneckJWLeeEALiuJLiuXLudvigJNeuendorfferSSachsSXiongYTaming heterogeneity — the Ptolemy approachProceedings of the IEEE200391112714410.1109/JPROC.2002.805829
– reference: CudennecLDubrullePGaleaFGoubierTSirdeyRGenerating code and memory buffers to reorganize data on many-core architecturesProcedia Computer Science2014291123113310.1016/j.procs.2014.05.101
– reference: Lee, E.A. (1991). Consistency in dataflow graphs IEEE Transactions on Parallel and Distributed Systems 2(2).
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Snippet In dataflow representations for signal processing systems, applications are represented as directed graphs in which vertices represent computations and edges...
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SubjectTerms Apexes
Buffers
Circuits and Systems
Computer Imaging
Data transmission
Electrical Engineering
Engineering
Engineering Sciences
Graph representations
Graph theory
Graphical representations
Image Processing and Computer Vision
Memory management
Pattern Recognition
Pattern Recognition and Graphics
Signal processing
Signal,Image and Speech Processing
Vision
Title Passive-Active Flowgraphs for Efficient Modeling and Design of Signal Processing Systems
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