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NETRA: a hierarchical and partitionable architecture for computer vision systems

机译：NETRA：用于计算机视觉系统的分层且可划分的体系结构

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摘要

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.
机译：计算机视觉被认为是最复杂和计算最密集的问题之一。通常，计算机视觉系统（CVS）尝试根据模型来关联场景。典型的CVS采用非常广泛的算法，例如数值，图像处理，图形算法，符号处理和人工智能。作者提出了一种用于计算机视觉系统的多处理器体系结构，称为“ NETRA”。 NETRA是高度灵活的体系结构。 NETRA的拓扑是递归定义的，因此很容易从小型系统扩展到大型系统。它是具有树型控件层次结构的层次结构。它的叶节点由处理器集群组成，这些处理器集群与具有可选广播功能的可编程纵横制交换机相连，以提供所需的灵活性。集群中的处理器可以以SIMD，MIMD或类似Systolic的模式运行。该体系结构的其他功能包括将有限的数据驱动计算集成到主要的控制流机制，块级控制和数据流，内存管理功能的分散化以及分层负载平衡和调度功能中。本文还提出了NETRA集群的定性评估和初步性能结果。

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