Deep Learning Classifiers with Memristive Networks

Theory and Applications

Buch | Hardcover

XIII, 213 Seiten

2019 | 1st ed. 2020
Springer International Publishing (Verlag)
978-3-030-14522-4 (ISBN)

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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.

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Erscheinungsdatum	18.04.2019
Reihe/Serie	Modeling and Optimization in Science and Technologies
Zusatzinfo	XIII, 213 p. 124 illus., 102 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	508 g
Themenwelt	Informatik ► Datenbanken ► Data Warehouse / Data Mining
	Informatik ► Grafik / Design ► Digitale Bildverarbeitung
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Technik
Schlagworte	Deep Learning Algorithms • Deep Neuro-fuzzy Networks • DNN- based Models for Speech Recognition • Gradient Descent Algorithm • Hierarchical Temporal Memories • Memristive Convolutional Neural Network • Memristive Crossbar Arrays • Memristive Deep Neural Networks • Memristive Edge Computing • Memristive Long Short Term Memory • Memristor Materials • Memristor Models • Memristor Multi-level Memories • Modular Crossbar Array • Neural Network Classifiers • Neuro-memristive Computing
ISBN-10	3-030-14522-0 / 3030145220
ISBN-13	978-3-030-14522-4 / 9783030145224
Zustand	Neuware