Cover is for reference only

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...

Full description

Saved in:
Bibliographic Details
Corporate Authors: SpringerLink Online service
Group Author: Yu, Shimeng
Published: Springer International Publishing : Imprint: Springer,
Publisher Address: Cham :
Publication Dates: 2017.
Literature type: eBook
Language: English
Subjects:
Engineering
Microprocessors
Electronic circuits
Circuits and Systems
Electronic Circuits and Devices
Processor Architectures
Online Access: http://dx.doi.org/10.1007/978-3-319-54313-0
Summary: 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 summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, var
Carrier Form: 1 online resource (XI, 269 pages): illustrations
ISBN: 9783319543130
Index Number: TK7888
CLC: TP331
Contents: 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 Memristi

Related Book Recommendations