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

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書目名稱Deep Learning Classifiers with Memristive Networks影響因子(影響力)




書目名稱Deep Learning Classifiers with Memristive Networks影響因子(影響力)學科排名




書目名稱Deep Learning Classifiers with Memristive Networks網絡公開度




書目名稱Deep Learning Classifiers with Memristive Networks網絡公開度學科排名




書目名稱Deep Learning Classifiers with Memristive Networks被引頻次




書目名稱Deep Learning Classifiers with Memristive Networks被引頻次學科排名




書目名稱Deep Learning Classifiers with Memristive Networks年度引用




書目名稱Deep Learning Classifiers with Memristive Networks年度引用學科排名




書目名稱Deep Learning Classifiers with Memristive Networks讀者反饋




書目名稱Deep Learning Classifiers with Memristive Networks讀者反饋學科排名

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.

留戀

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

柔軟

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.