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The Resource Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices

Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices

Label
Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices
Title
Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices
Creator
Subject
Language
eng
Member of
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Cognitive Systems Monographs
Series volume
v.31
Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices
Label
Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4790542
Publication
Copyright
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface -- Contents -- Dr. Manan Suri -- Hardware Spiking Artificial Neurons, Their Response Function, and Noises -- 1 Introduction -- 1.1 Biological Neurons -- 1.2 Neuronal Response Function -- 1.3 Neuronal Noises -- 1.4 Artificial Neuron Models -- 2 Hardware Spiking Neurons -- 2.1 Silicon Neurons -- 2.2 Emerging Spiking Neurons -- 3 Summary and Outlook -- References -- Synaptic Plasticity with Memristive Nanodevices -- 1 Introduction -- 2 Neuromorphic Systems: Basic Processing and Data Representation -- 2.1 Data Encoding in Neuromorphic Systems -- 2.2 Spike Computing for Neuromorphic Systems -- 3 Synaptic Plasticity for Information Computing -- 3.1 Causal Approach: Synaptic Learning Versus Synaptic Adaptation -- 3.2 Phenomenological Approach: Short-Term Plasticity Versus Long-Term Plasticity -- 4 Synaptic Plasticity Implementation in Neuromorphic Nanodevices -- 4.1 Causal Implementation of Synaptic Plasticity -- 4.2 Phenomenological Implementation of Synaptic Plasticity -- 5 Conclusions -- References -- Neuromemristive Systems: A Circuit Design Perspective -- 1 Introduction: Taking a Cue from Nature -- 2 Memristor Overview -- 3 Voltage Versus Current-Mode Circuit Designs for NMSs -- 4 Neuron Circuits: Primary Information Processing Units -- 4.1 Input Stage -- 4.2 Activation Function -- 5 Synapse Circuits: Communication and Memory -- 6 Plasticity Circuits: Adaptation/Learning -- 7 Summary and Outlook -- References -- Memristor-Based Platforms: A Comparison Between Continous-Time and Discrete-Time Cellular Neural Networks -- 1 Introduction -- 2 Backgorund -- 3 New Memristance Restoring Circuit -- 4 Simulation Results -- 5 Cellular Automata and DTCNNs -- 6 Belief Propagation Inspired Algorithm and Cellular Automaton Equivalence for RGB Image Processing -- 7 Element Detection in RGB Image -- 8 Conclusions -- References
  • Reinterpretation of Magnetic Tunnel Junctions as Stochastic Memristive Devices -- 1 Introduction -- 2 Magnetic Tunnel Junction Basics -- 2.1 Basic Structure of Magnetic Tunnel Junctions -- 2.2 Integration and Scaling Potential of STT-MTJs -- 2.3 Physical Modeling of Magnetization Dynamics -- 2.4 Models About the Statistics of MTJs Switching Delay -- 3 MTJs as Stochastic Synapses -- 3.1 Example of a Feed-Forward Spiking Neural Network Using MTJ-based Synapses -- 3.2 Impact of the Device Properties on the System Operation -- 4 Conclusion -- References -- Multiple Binary OxRAMs as Synapses for Convolutional Neural Networks -- 1 Multiple Binary OxRAM Devices as Artificial Synapses -- 2 Convolutional Neural Network Architecture -- 3 Synaptic Weight Resolution and Tolerance to Variability -- 4 Conclusions -- References -- Nonvolatile Memory Crossbar Arrays for Non-von Neumann Computing -- 1 Introduction -- 2 Considerations for a Crossbar Implementation -- 3 Phase-Change Memory (PCM): Results -- 3.1 Experimental Results -- 4 Non-filamentary RRAM Results -- 4.1 Fabrication of PCMO Devices -- 4.2 Simulation Results -- 5 Discussion -- 6 Conclusions -- References -- Novel Biomimetic Si Devices for Neuromorphic Computing Architecture -- 1 Motivation -- 2 Biological Systems, Computing Algorithms, and Electronic Hardware Equivalents -- 2.1 Synapse -- 2.2 Neuron -- 3 Conclusions -- References -- Exploiting Variability in Resistive Memory Devices for Cognitive Systems -- 1 Introduction -- 2 Nanoscale Filamentary RRAM -- 2.1 RRAM Resistance Variability -- 2.2 Cycle-to-Cycle Variability -- 3 Extreme Learning Machine -- 3.1 Basics of Extreme Learning Machines (ELM) -- 3.2 Proposed OxRAM-ELM Architecture -- 3.3 Results -- 4 CMOS-RRAM Restricted Boltzmann Machine -- 4.1 Restricted Boltzmann Machines -- 4.2 Proposed RBM Architecture -- 4.3 Results and Discussion
  • 5 Conclusion -- References -- Theoretical Analysis of Spike-Timing-Dependent Plasticity Learning with Memristive Devices -- 1 Introduction -- 2 Spike-Timing-Dependent Plasticity and Expectation-Maximization -- 2.1 Simplified STDP -- 2.2 Connection with Expectation-Maximization -- 3 Impact of Device Physics -- 3.1 Cumulative Memristive Synapses -- 3.2 Stochastic Synapses -- 4 Robustness of STDP Learning -- 5 Conclusion -- References
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{'f': 'http://opac.lib.rpi.edu/record=b4370783'}
Extent
1 online resource (217 pages)
Form of item
online
Isbn
9788132237037
Media category
computer
Media MARC source
rdamedia
Media type code
c
Sound
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Specific material designation
remote

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