Parallel architectures for vision

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Abstract
Options are examined that drive the design of a vision-oriented computer, beginning with the analysis of the basic vision computation and communication requirements. The classical taxonomy is briefly reviewed for parallel computers, based on the instruction and data stream. A recently proposed criterion, the degree of autonomy of each processor, is applied to further classify fine-grain SIMD (single-instruction, multiple-data-stream) massively parallel computers. Three types of processor autonomy, namely, operational autonomy, addressing autonomy, and connection autonomy, are identified. For each type, the basic definition is given and some examples shown. The concept of connection autonomy, which is believed to be the key point in the development of massively parallel architectures for vision, is presented. Two examples are shown of parallel computers featuring different types of connection autonomy-the Connection Machine and the polymorphic-Torus-and their cost and benefits are compared

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