Associative processing based on content-addressable memories has been argued to be the natural solution for nonnumerical information processing applications. Unfortunately, the implementation requirements of these architectures when one uses conventional electronic technology have been cost prohibitive; therefore associative processors have not been realized. Instead, software methods that emulate the behavior of associative processing have been promoted and mapped onto conventional location-addressable systems. However, this does not bring about the natural parallelism of associative processing, namely, the ability to access many data words simultaneously. Optics has the advantage over electronics of directly supporting associative processing by providing economic and efficient interconnects, massive parallelism, and high-speed processing. The principles of designing an optical content-addressable parallel processor (OCAPP) for the efficient support of parallel symbolic computing are presented. The architecture is designed to exploit optics advantages fully in interconnects and high-speed operations. Several parallel search-and-retrieval algorithms are mapped onto an OCAPP to illustrate its capability of supporting parallel symbolic computing. A theoretical performance analysis of these algorithms is presented. This analysis reveals that the execution times of the parallel algorithms presented are independent of the problem size, which makes the OCAPP suitable for applications in which the number of data sets to be operated on is high (e.g., massive parallel processing). A preliminary optical implementation of the architecture with currently available optical components is also presented.
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