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Nearest-Neighbor Non-Iterative Error Correcting Optical Associative Memory Processor

The Hopfield neural network model has recently been proposed as a method for optically determining the nearest-neighbor of a binary bipolar test vector from a set of binary bipolar reference vectors. We illustrate several drawbacks of this approach and introduce a new technique called direct storage nearest-neighbor (DSNN) algorithm to accomplish the same task. We provide a comparison of the two approaches and demonstrate the superiority of the proposed DSNN algorithm.