NETRA: A Hierarchical and Partitionable Architecture for Computer Vision Systems

Alok N. Choudhary, Janak H. Patel, Narendra Ahuja

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Computer vision is regarded as one of the most complex and computationally intensive problems. In general, a Computer Vision System (CVS) attempts to relate scene(s) in terms of model(s). A typical CVS employs algorithms from a very broad spectrum such as such as numerical, image processing, graph algorithms, symbolic processing, and artificial intelligence. This paper presents a multiprocessor architecture, called “NE-TRA,” for computer vision systems. NETRA is a highly flexible architecture. The topology of NETRA is recursively defined, and hence, is easily scalable from small to large systems. It is a hierarchical architecture with a tree-type control hierarchy. Its leaf nodes consists of a cluster of processors connected with a programmable crossbar with selective broadcast capability to provide the desired flexibility. The processors in clusters can operate in SIMD-, MIMD- or Systolic-like modes. Other features of the architecture include integration of limited data-driven computation within a primarily control flow mechanism, block-level control and data flow, decentralization of memory management functions, and hierarchical load balancing and scheduling capabilities. This paper also presents a qualitative evaluation and preliminary performance results of a cluster of NETRA.

Original languageEnglish (US)
Pages (from-to)1092-1104
Number of pages13
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number10
StatePublished - Oct 1993


  • Computer vision
  • parallel algorithms
  • parallel architectures
  • partitionable architectures
  • performance evaluation

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics


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