Deep Learning Classifiers with Memristive Networks

Theory and Applications

  • Alex PappachenĀ James

Part of the Modeling and Optimization in Science and Technologies book series (MOST, volume 14)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Foundations and System Applications

    1. Front Matter
      Pages 1-1
    2. Alex Pappachen James
      Pages 3-12
    3. Olga Krestinskaya, Aidana Irmanova, Alex Pappachen James
      Pages 13-40
    4. Adilya Bakambekova, Alex Pappachen James
      Pages 41-55
    5. Yeldar Toleubay, Alex Pappachen James
      Pages 57-67
    6. Akzharkyn Izbassarova, Aziza Duisembay, Alex Pappachen James
      Pages 69-79
    7. Damira Pernebayeva, Alex Pappachen James
      Pages 81-88
  3. Memristor Logic and Neural Networks

    1. Front Matter
      Pages 89-89
    2. Olga Krestinskaya, Alex Pappachen James
      Pages 91-102
    3. Aidana Irmanova, Serikbolsyn Myrzakhmet, Alex Pappachen James
      Pages 103-116
    4. Irina Dolzhikova, Akshay Kumar Maan, Alex Pappachen James
      Pages 117-130
    5. Olga Krestinskaya, Alex Pappachen James
      Pages 131-137
    6. Kamilya Smagulova, Alex Pappachen James
      Pages 139-153
    7. Kazybek Adam, Kamilya Smagulova, Alex Pappachen James
      Pages 155-167
    8. Yeldos Dauletkhanuly, Olga Krestinskaya, Alex Pappachen James
      Pages 169-180
    9. Olga Krestinskaya, Irina Dolzhikova, Alex Pappachen James
      Pages 181-194
    10. Anuar Dorzhigulov, Alex Pappachen James
      Pages 195-213

About this book


This book introduces readers to the fundamentals of deep neural network architectures, with a special emphasis on memristor circuits and systems. At first, the book offers an overview of neuro-memristive systems, including memristor devices, models, and theory, as well as an introduction to deep learning neural networks such as multi-layer networks, convolution neural networks, hierarchical temporal memory, and long short term memories, and deep neuro-fuzzy networks. It then focuses on the design of these neural networks using memristor crossbar architectures in detail. The book integrates the theory with various applications of neuro-memristive circuits and systems. It provides an introductory tutorial on a range of issues in the design, evaluation techniques, and implementations of different deep neural network architectures with memristors.


Neuro-memristive Computing Memristive Crossbar Arrays Memristor Models Memristor Materials Deep Learning Algorithms Neural Network Classifiers Gradient Descent Algorithm DNN- based Models for Speech Recognition Memristor Multi-level Memories Memristive Long Short Term Memory Memristive Deep Neural Networks Deep Neuro-fuzzy Networks Memristive Convolutional Neural Network Modular Crossbar Array Hierarchical Temporal Memories Memristive Edge Computing

Editors and affiliations

  • Alex PappachenĀ James
    • 1
  1. 1.School of EngineeringNazarbayev UniversityAstanaKazakhstan

Bibliographic information