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© 2020

Deep Learning Classifiers with Memristive Networks

Theory and Applications

  • Alex Pappachen James

Benefits

  • Offers an introduction to deep neural network architectures

  • Describes in detail different kind of neuro-memristive systems, circuits and models

  • Shows how to implement different kind of neural networks in analog memristive circuits

Book

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

Introduction

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.

Keywords

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 Pappachen James
    • 1
  1. 1.School of EngineeringNazarbayev UniversityAstanaKazakhstan

About the editors

Bibliographic information

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