Deep-learning-based data page classification for holographic memory
Tomoyoshi Shimobaba, Naoki Kuwata, Mizuha Homma, Takayuki Takahashi, Yuki Nagahama, Marie Sano, Satoki Hasegawa, Ryuji Hirayama, Takashi Kakue, Atsushi Shiraki, Naoki Takada, Tomoyoshi Ito
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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%, whereas the deep neural network was able to classify data pages at an accuracy of 99.98%. The accuracy of the deep neural network is two orders of magnitude better than the MLP.