In this paper, an optical implementation of an associative memory based on parallel rank-one interconnections and time integration is proposed and experimental results are demonstrated. Optical parallel rank-one interconnections based on singular value decomposition and time-integration are useful in implementing real-time, programmable, and adaptable neural networks with arbitrary order. Input patterns can have the same size as the outer-product memory matrix at the expense of time usage proportional to the dimensionality of the input pattern. Computer simulation results are given to show that this time usage can be reduced by using singular value decomposition and principal components analysis, without substantial degradation of the performance of the system. This consequently allows efficient utilization of the spatial parallelism that optical systems can offer.
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