Deep Learning Classifiers with Memristive Networks

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

• School of Engineering, Nazarbayev University, Astana, Kazakhstan

  Alex Pappachen James

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