Neuro-inspired Computing Using Resistive Synaptic Devices
This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art sum...
Συγγραφή απο Οργανισμό/Αρχή: | |
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Άλλοι συγγραφείς: | |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
Cham :
Springer International Publishing : Imprint: Springer,
2017.
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Θέματα: | |
Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.