Who is the Winner? Memristive-CMOS Hybrid Modules: CNN-LSTM Versus HTM

Kamilya Smagulova, Olga Krestinskaya, Alex James

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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 languageEnglish
Article number8917714
Pages (from-to)164-172
Number of pages9
JournalIEEE Transactions on Biomedical Circuits and Systems
Issue number2
Publication statusPublished - Apr 2020


  • 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

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