Deep-learning-based data page classification for holographic memory
This work addresses data retrieval accuracy for holographic memory systems, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackled the problem of classifying data pages in holographic memory under noisy and shifted conditions, achieving a classification accuracy of 99.98% with a deep neural network, which is two orders of magnitude better than a conventional MLP at 91.58%.
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.