Deep Learning Classifiers with Memristive Networks: Theory and Applications


Deep Learning Classifiers with Memristive Networks: Theory and Applications
By 作者: Alex Pappachen James
pages 页数: 213 pages
Edition 版本: 1
Language 语言: English
Publisher Finelybook 出版社: Springer
Publication Date 出版日期: 2019-04-09
ISBN-10 书号:3030145220
ISBN-13 书号:9783030145224
Book Description to Finelybook sorting
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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