This paper introduces programmable arrays of optically inter-connected electronic processors and compares them with conventional symbolic substitution (SS) systems. The comparison is made on the basis of computational efficiency, speed, size, energy utilization, programmability, and fault tolerance. The small grain size and space-invariant connections of SS lead to poor computational efficiency, difficult programming, and difficult incorporation of fault tolerance. Reliance on optical gates as its fundamental building elements is shown to give poor energy utilization. Programmable optoelectronic multiprocessor (POEM) systems, on the other hand, provide the architectural flexibility for good computational efficiency, use an energy-efficient combination of technologies, and support traditional programming methodologies and fault tolerance. Although the inherent clock speed of POEM systems is slower than that of SS systems, for most problems they will provide greater computational throughput. This comparison does not take into account the recent addition of crossover interconnect and space-variant masks to the SS architecture.

Programmable Optoelectronic Multiprocessors And Their Comparison With Symbolic Substitution For Digital Optical Computing

Storage, interconnection, and processing are discussed. Various types of optical disks and page-oriented holographic memories are considered. It is shown that optical storage is advancing rapidly and holds the potential of hundreds of megabytes per second data rates from a single storage unit, which can provide many new opportunities for supercomputing. Module-to-module, board-to-board, and chip-to-chip interconnection and gate-to-gate communication are discussed. It is concluded that optical interconnection is, in many cases, superior to electronic interconnection and holds the key to the development of future electrooptic systems. Optical computing devices are discussed and various application areas where optical processing as well as storage and interconnection are expected to play a role in the future are considered. The authors believe that optical processing, while holding considerable promise, lags behind its electronic counterpart primarily due to the fact that digital optical device development is in its infancy. They predict near-term systems will be electrooptic, with each technology providing its strength to the problem at hand. >

Optics and supercomputing

An optical architecture that uses symbolic substitution to perform morphological transformations is proposed. It is shown how the four basic morphological transformation operations can be posed as symbolic substitution problems. Representative examples of the application of morphological transformations to image processing are given.

Optical symbolic substitution for morphological transformations.

An optoelectronic implementation based on optical neighborhood operations and electronic nonlinear feedback is proposed to perform morphological image processing such as erosion, dilation, opening, closing, and edge detection. Results of a numerical simulation are given and experimentally verified.

Optoelectronic implementation of mathematical morphology.

Based on the parallel-processing and interconnection capabilities of optics, a novel, compact, real-time programmable parallel optical morphological filter is proposed and demonstrated Using a real-time polarization-encoded image-casting scheme, some proof-of-principle experimental results are presented

Compact parallel real-time programmable optical morphological image processor

Optical hardware for symbolic substitution is under serious consideration for parallel optical computing applications. Symbolic substitution replaces chosen patterns of ones and zeros in a binary array with other chosen patterns of ones and zeros. Implemented with specialized substitution rules, symbolic substitution can be applied to the processing of imges that are represented in binary form. Important operations investigated in this paper are (1) nonlinear filtering operations applied to shapes (morphological transformations, including erosion, dilation, opening, closing) and (2) linear filtering operations applied to binary digital representations of continuous-tone images. Examples presented include a nonlinear noise-removal operation, thresholding, a gradient operator, and convolution. The results of an engineering study of system complexity for linear filtering operations are also presented.

Symbolic substitution applications to image processing

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Symbolic substitution applications to image processing