Deep-learning-based data page classification for holographic memory
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We propose a deep-learning-based classification of data pages used in holographic memory. We numerically investigated the classification performance of a conventional multi-layer perceptron (MLP) and a deep neural network, under the condition that reconstructed page data are contaminated by some noise and are randomly laterally shifted. The MLP was found to have a classification accuracy of 91.58 pages at an accuracy of 99.98 orders of magnitude better than the MLP.
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