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- W2990695486 abstract "Hierarchical, modular and sparse information processing are signature characteristics of biological neural networks. These aspects have been the backbone of several artificial neural network designs of the brain-like networks, including Hierarchical Temporal Memory (HTM). The main contribution of this work is showing that Convolutional Neural Network (CNN) in combination with Long short term memory (LSTM) can be a good alternative for implementing the hierarchy, modularity and sparsity of information processing. To demonstrate this, we draw a comparison of CNN-LSTM and HTM performance on a face recognition problem with a small training set. We also present the analog CMOS-memristor circuit blocks required to implement such a scheme. The presented memristive implementations of the CNN-LSTM architecture are easier to i mplement, train and offer higher recognition performance than the HTM. The study also includes memristor variability and failure analysis." @default.
- W2990695486 created "2019-12-05" @default.
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- W2990695486 date "2020-04-01" @default.
- W2990695486 modified "2023-10-18" @default.
- W2990695486 title "Who is the Winner? Memristive-CMOS Hybrid Modules: CNN-LSTM Versus HTM" @default.
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- W2990695486 doi "https://doi.org/10.1109/tbcas.2019.2956435" @default.
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