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.
|Original language||English (US)|
|Number of pages||9|
|Journal||IEEE Transactions on Biomedical Circuits and Systems|
|State||Published - Apr 1 2020|