TY - JOUR
T1 - NETRA
T2 - A Hierarchical and Partitionable Architecture for Computer Vision Systems
AU - Choudhary, Alok N.
AU - Patel, Janak H.
AU - Ahuja, Narendra
N1 - Funding Information:
Manuscript received July 16, 1991; revised August 10, 1992. This work was supported in part by National Aeronautics and Space Administration Under Contract NASA NAG-1-613. A. N. Choudhary is with the Department of Electrical and Computer Engineering, Syracuse University, Syracuse, NY 13244. J. H. Patel is with the Center for Reliable and High Performance Computing, University of Illinois, Urbana-Champaign, Urbana, IL 61801. N. Ahuja is with Beckman Institute, University of Illinois, Urbana-Champaign, Urbana, IL 61801. IEEE Log Number 9213476.
PY - 1993/10
Y1 - 1993/10
N2 - 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.
AB - 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.
KW - Computer vision
KW - parallel algorithms
KW - parallel architectures
KW - partitionable architectures
KW - performance evaluation
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U2 - 10.1109/71.246071
DO - 10.1109/71.246071
M3 - Article
AN - SCOPUS:0027678189
SN - 1045-9219
VL - 4
SP - 1092
EP - 1104
JO - IEEE Transactions on Parallel and Distributed Systems
JF - IEEE Transactions on Parallel and Distributed Systems
IS - 10
ER -