Titlebook: Deep Learning Classifiers with Memristive Networks; Theory and Applicati Alex Pappachen James Book 2020 Springer Nature Switzerland AG 2020

万能

书目名称Deep Learning Classifiers with Memristive Networks影响因子(影响力)




书目名称Deep Learning Classifiers with Memristive Networks影响因子(影响力)学科排名




书目名称Deep Learning Classifiers with Memristive Networks网络公开度




书目名称Deep Learning Classifiers with Memristive Networks网络公开度学科排名




书目名称Deep Learning Classifiers with Memristive Networks被引频次




书目名称Deep Learning Classifiers with Memristive Networks被引频次学科排名




书目名称Deep Learning Classifiers with Memristive Networks年度引用




书目名称Deep Learning Classifiers with Memristive Networks年度引用学科排名




书目名称Deep Learning Classifiers with Memristive Networks读者反馈




书目名称Deep Learning Classifiers with Memristive Networks读者反馈学科排名

Neutral-Spine

Generator

凌辱

jeopardize

Introduction to Neuro-Memristive Systemsions that extend the capabilities of exiting computing hardware. The full potential of neuro-memristive systems is yet to be completely realised and could provide ways to develop higher level of socially engineered machine cognition.

embolus

embolus

Multi-level Memristive Memory for Neural Networksand architecture level issues force memory engineers to approach memristive memory design in different ways. In this chapter device-level problems: restricted number of resistance states, stochastic switching and architecture level problem: sneak paths will be discussed, and their state of the art solutions will be presented.

留恋

2196-7326 els.Shows how to implement different kind of neural 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 device

arthroscopy

柔软

Design for Six Sigma + LeanToolsetlows extending the capabilities of threshold logic circuits. In this chapter, we review the hardware designs of memristive threshold logic (MTL) circuits that are inspired by the principle of neuron firing inside the brain. Variety of threshold architectures, their limitations and possible field of application are discussed.