Deep Learning Classifiers with Memristive Networks: Theory and Applications (Modeling and Optimization in Science and Technologies, #14)

Alex Pappachen James (Editor)

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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.

  • ISBN13 9783030145224
  • Publish Date 17 April 2019
  • Publish Status Active
  • Publish Country CH
  • Imprint Springer Nature Switzerland AG
  • Edition 1st ed. 2020
  • Format Hardcover
  • Pages 213
  • Language English