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arXiv · 1707.00684

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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Abstract

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.

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Tomoyoshi Shimobaba, Naoki Kuwata, Mizuha Homma, Takayuki Takahashi, Yuki Nagahama, Marie Sano, Satoki Hasegawa, Ryuji Hirayama, Takashi Kakue, Atsushi Shiraki, Naoki Takada, Tomoyoshi Ito. 2017-07-02. Deep-learning-based data page classification for holographic memory. https://doi.org/10.1364/ao.56.007327

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