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.
Original language | English |
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Article number | 8917714 |
Pages (from-to) | 164-172 |
Number of pages | 9 |
Journal | IEEE Transactions on Biomedical Circuits and Systems |
Volume | 14 |
Issue number | 2 |
DOIs | |
Publication status | Published - Apr 2020 |
Keywords
- CMOS-memristive network
- convolutional neural network
- hierarchical temporal memory
- long short temp memory
- spatio-temporal processing
ASJC Scopus subject areas
- Biomedical Engineering
- Electrical and Electronic Engineering