NETRA: a hierarchical and partitionable architecture for computer vision systems

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 numerical, image processing, graph algorithms, symbolic processing, and artificial intelligence. The authors present a multiprocessor architecture, called “NETRA,” 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. The paper also presents a qualitative evaluation and preliminary performance results of a cluster of NETRA