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Neuro-inspired computing using resis...

Yu, Shimeng.

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Neuro-inspired computing using resistive synaptic devices

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neuro-inspired computing using resistive synaptic devices/ edited by Shimeng Yu.
other author:	Yu, Shimeng.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xi, 269 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter1: Introduction to Neuro-Inspired Computing using Resistive Synaptic Devices -- Part I: Device-level Demonstrations of Resistive Synaptic Devices -- Chapter2: Phase Change Memory based Synaptic Devices -- Chapter3: Pr0.7Ca0.3MnO3 (PCMO) based Synaptic Devices -- Chapter4: TaOx/TiO2 based Synaptic Devices -- Part II: Array-level Demonstrations of Resistive Synaptic Devices and Neural Networks -- Chapter5: Training and Inference in Hopfield Network using 10×10 Phase Change Synaptic Array -- Chapter6: Experimental Demonstration of Firing-Rate Neural Networks based on Metal-Oxide Memristive Crossbars -- Chapter7: Weight Tuning of Resistive Synaptic Devices and Convolution Kernel Operation on 12×12 Cross-Point Array -- Chapter8: Spiking Neural Network with 256×256 PCM Array -- Part III: Circuit, Architecture and Algorithm-level Design of Resistive Synaptic Devices based Neuromorphic System -- Chapter9: Peripheral Circuit Design Considerations of Neuro-inspired Architectures -- Chapter10: Processing-in-Memory Architecture Design for Accelerating Neuro-Inspired Algorithms -- Chapter11: Multi-layer Perceptron Algorithm: Impact of Non-Ideal Conductance and Area-Efficient Peripheral Circuits -- Chapter12: Impact of Non-Ideal Resistive Synaptic Device Behaviors on Implementation of Sparse Coding Algorithm -- Chapter13: Binary OxRAM/CBRAM Memories for Efficient Implementations of Embedded Neuromorphic Circuits.
Contained By:	Springer eBooks
Subject:	Neural computers. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-54313-0
ISBN:	9783319543130

Neuro-inspired computing using resistive synaptic devices
Neuro-inspired computing using resistive synaptic devices[electronic resource] /edited by Shimeng Yu. - Cham :Springer International Publishing :2017. - xi, 269 p. :ill. (some col.), digital ;24 cm.

Chapter1: Introduction to Neuro-Inspired Computing using Resistive Synaptic Devices -- Part I: Device-level Demonstrations of Resistive Synaptic Devices -- Chapter2: Phase Change Memory based Synaptic Devices -- Chapter3: Pr0.7Ca0.3MnO3 (PCMO) based Synaptic Devices -- Chapter4: TaOx/TiO2 based Synaptic Devices -- Part II: Array-level Demonstrations of Resistive Synaptic Devices and Neural Networks -- Chapter5: Training and Inference in Hopfield Network using 10×10 Phase Change Synaptic Array -- Chapter6: Experimental Demonstration of Firing-Rate Neural Networks based on Metal-Oxide Memristive Crossbars -- Chapter7: Weight Tuning of Resistive Synaptic Devices and Convolution Kernel Operation on 12×12 Cross-Point Array -- Chapter8: Spiking Neural Network with 256×256 PCM Array -- Part III: Circuit, Architecture and Algorithm-level Design of Resistive Synaptic Devices based Neuromorphic System -- Chapter9: Peripheral Circuit Design Considerations of Neuro-inspired Architectures -- Chapter10: Processing-in-Memory Architecture Design for Accelerating Neuro-Inspired Algorithms -- Chapter11: Multi-layer Perceptron Algorithm: Impact of Non-Ideal Conductance and Area-Efficient Peripheral Circuits -- Chapter12: Impact of Non-Ideal Resistive Synaptic Device Behaviors on Implementation of Sparse Coding Algorithm -- Chapter13: Binary OxRAM/CBRAM Memories for Efficient Implementations of Embedded Neuromorphic Circuits.

ISBN: 9783319543130

Standard No.: 10.1007/978-3-319-54313-0doiSubjects--Topical Terms:

535411
Neural computers.

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Neuro-inspired computing using resistive synaptic devices
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245 0 0 $a Neuro-inspired computing using resistive synaptic devices $h [electronic resource] / $c edited by Shimeng Yu.
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505 0 $a Chapter1: Introduction to Neuro-Inspired Computing using Resistive Synaptic Devices -- Part I: Device-level Demonstrations of Resistive Synaptic Devices -- Chapter2: Phase Change Memory based Synaptic Devices -- Chapter3: Pr0.7Ca0.3MnO3 (PCMO) based Synaptic Devices -- Chapter4: TaOx/TiO2 based Synaptic Devices -- Part II: Array-level Demonstrations of Resistive Synaptic Devices and Neural Networks -- Chapter5: Training and Inference in Hopfield Network using 10×10 Phase Change Synaptic Array -- Chapter6: Experimental Demonstration of Firing-Rate Neural Networks based on Metal-Oxide Memristive Crossbars -- Chapter7: Weight Tuning of Resistive Synaptic Devices and Convolution Kernel Operation on 12×12 Cross-Point Array -- Chapter8: Spiking Neural Network with 256×256 PCM Array -- Part III: Circuit, Architecture and Algorithm-level Design of Resistive Synaptic Devices based Neuromorphic System -- Chapter9: Peripheral Circuit Design Considerations of Neuro-inspired Architectures -- Chapter10: Processing-in-Memory Architecture Design for Accelerating Neuro-Inspired Algorithms -- Chapter11: Multi-layer Perceptron Algorithm: Impact of Non-Ideal Conductance and Area-Efficient Peripheral Circuits -- Chapter12: Impact of Non-Ideal Resistive Synaptic Device Behaviors on Implementation of Sparse Coding Algorithm -- Chapter13: Binary OxRAM/CBRAM Memories for Efficient Implementations of Embedded Neuromorphic Circuits.
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