Overview
- 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
Part of the book series: Modeling and Optimization in Science and Technologies (MOST, volume 14)
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Table of contents (15 chapters)
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Front Matter
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Foundations and System Applications
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Front Matter
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Memristor Logic and Neural Networks
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Front Matter
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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
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.
Editors and Affiliations
About the editor
Bibliographic Information
Book Title: Deep Learning Classifiers with Memristive Networks
Book Subtitle: Theory and Applications
Editors: Alex Pappachen James
Series Title: Modeling and Optimization in Science and Technologies
DOI: https://doi.org/10.1007/978-3-030-14524-8
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-14522-4Published: 17 April 2019
eBook ISBN: 978-3-030-14524-8Published: 08 April 2019
Series ISSN: 2196-7326
Series E-ISSN: 2196-7334
Edition Number: 1
Number of Pages: XIII, 213
Number of Illustrations: 22 b/w illustrations, 102 illustrations in colour
Topics: Computational Intelligence, Pattern Recognition, Data Mining and Knowledge Discovery, Image Processing and Computer Vision