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Neuro-inspired Computing Using Resistive Synaptic Devices

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Cover of 'Neuro-inspired Computing Using Resistive Synaptic Devices'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Neuro-Inspired Computing Using Resistive Synaptic Devices
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    Chapter 2 Synaptic Devices Based on Phase-Change Memory
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    Chapter 3 Pr0.7Ca0.3MnO3 (PCMO)-Based Synaptic Devices
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    Chapter 4 TaOx-/TiO2-Based Synaptic Devices
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    Chapter 5 Training and Inference in Hopfield Network Using 10 × 10 Phase Change Synaptic Array
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    Chapter 6 Experimental Demonstration of Firing Rate Neural Networks Based on Metal-Oxide Memristive Crossbars
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    Chapter 7 Weight Tuning of Resistive Synaptic Devices and Convolution Kernel Operation on 12 × 12 Cross-Point Array
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    Chapter 8 Spiking Neural Network with 256 × 256 PCM Array
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    Chapter 9 Peripheral Circuit Design Considerations of Neuro-inspired Architectures
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    Chapter 10 Processing-In-Memory Architecture Design for Accelerating Neuro-Inspired Algorithms
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    Chapter 11 Multilayer Perceptron Algorithm: Impact of Nonideal Conductance and Area-Efficient Peripheral Circuits
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    Chapter 12 Impact of Nonideal Resistive Synaptic Device Behaviors on Implementation of Sparse Coding Algorithm
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    Chapter 13 Binary OxRAM/CBRAM Memories for Efficient Implementations of Embedded Neuromorphic Circuits
Attention for Chapter 9: Peripheral Circuit Design Considerations of Neuro-inspired Architectures
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