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  • Research Article
  • Open Access

Dataflow-Based Mapping of Computer Vision Algorithms onto FPGAs

  • 1Email author,
  • 1,
  • 1,
  • 1,
  • 2,
  • 2,
  • 1 and
  • 2
EURASIP Journal on Embedded Systems20072007:049236

  • Received: 1 May 2006
  • Accepted: 9 October 2006
  • Published:


We develop a design methodology for mapping computer vision algorithms onto an FPGA through the use of coarse-grain reconfigurable dataflow graphs as a representation to guide the designer. We first describe a new dataflow modeling technique called homogeneous parameterized dataflow (HPDF), which effectively captures the structure of an important class of computer vision applications. This form of dynamic dataflow takes advantage of the property that in a large number of image processing applications, data production and consumption rates can vary, but are equal across dataflow graph edges for any particular application iteration. After motivating and defining the HPDF model of computation, we develop an HPDF-based design methodology that offers useful properties in terms of verifying correctness and exposing performance-enhancing transformations; we discuss and address various challenges in efficiently mapping an HPDF-based application representation into target-specific HDL code; and we present experimental results pertaining to the mapping of a gesture recognition application onto the Xilinx Virtex II FPGA.


  • Design Methodology
  • Processing Application
  • Electronic Circuit
  • Gesture Recognition
  • Mapping Computer

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Authors’ Affiliations

Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA
Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, USA


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© Mainak Sen et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.