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Parallel 2-d convolution on a mesh connected array processor.

S Y Lee1, J K Aggarwal

  • 1Computer and Vision Research Center, College of Engineering, University of Texas at Austin, Austin, TX 78712.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

A novel parallel 2-D convolution scheme uses a mesh-connected array processor. This efficient method, suitable for VLSI, applies 1-D systolic concepts to image processing with arbitrary window sizes.

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Area of Science:

  • Computer Science
  • Image Processing
  • Parallel Computing

Background:

  • Two-dimensional (2-D) convolution is a fundamental operation in image processing.
  • Efficient parallel processing architectures are crucial for handling large image datasets.
  • Existing methods may face limitations in scalability and adaptability to various convolution window shapes.

Purpose of the Study:

  • To present a novel parallel 2-D convolution scheme.
  • To demonstrate the scheme's efficiency and suitability for VLSI implementation.
  • To extend the scheme for arbitrary convolution window sizes and shapes.

Main Methods:

  • A mesh-connected array processor architecture is utilized, with processing elements matching image pixel count.
  • The 1-D systolic concept is adapted for 2-D convolution on the mesh structure.
  • Computation proceeds along a defined 'convolution path' in a systolic manner.

Main Results:

  • The number of computation steps is comparable to the number of convolution window coefficients for many window types.
  • The scheme is adaptable to convolution windows of arbitrary size and shape.
  • Efficiency analysis confirms the effectiveness of the proposed convolution path, ideally a Hamiltonian path.

Conclusions:

  • The proposed parallel 2-D convolution scheme offers an efficient solution for image processing tasks.
  • Its simple architecture and control strategy are well-suited for VLSI (Very Large-Scale Integration) implementation.
  • The method provides a scalable and flexible approach to 2-D convolution.