Memristor Computing Systems

✍ Scribed by Leon O. Chua (editor), Ronald Tetzlaff (editor), Angela Slavova (editor)

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 307
• Edition: 1st ed. 2022
• Category: Library

✦ Synopsis

This contributed volume offers practical solutions and design-, modeling-, and implementation-related insights that address current research problems in memristors, memristive devices, and memristor computing.

The book studies and addresses related challenges in and proposes solutions for the future of memristor computing. State-of-the-art research on memristor modeling, memristive interconnections, memory circuit architectures,  software simulation tools, and applications of memristors in computing are presented. Utilising contributions from numerous experts in the field, written in clear language and illustrated throughout, this book is a comprehensive reference work.

Memristor Computing Systems explains memristors and memristive devices in an accessible way for graduate students and researchers with a basic knowledge of electrical and control systems engineering, as well as prompting further research for more experienced academics.

✦ Table of Contents

Preface
Introduction
Contents
Part I Memristor Computing Theory
1 Edge-of-Chaos in CNN Models with Memristor Synapses
1.1 Introduction
1.2 Nonlinear Convection–Diffusion Model
1.2.1 Memristor CNN Model of (1.1)
1.2.2 Edge-of-Chaos in the M-CNN Model
1.2.3 Applications of the M-CNN Model to Noise Removal
1.3 M-CNN Model of Nano-structures
1.3.1 Statement of the Problem
1.3.2 M-CNN Model of Boundary Value Problem (1.8), (1.9)
1.3.3 Edge-of-Chaos in M-CNN Model (1.12)
1.3.4 Simulations and Validation
1.4 Conclusions
References
2 Dynamic Analysis of Memristor Circuits via Input–Output Techniques
2.1 Introduction
2.2 Class of Circuits Description and Preliminaries
2.2.1 The Chua's Memristor Circuit
2.2.2 The Murali–Lakshmanan–Chua's Memristor Circuit
2.3 A Canonical Reduced-Order Input–Output Representation
2.3.1 Canonical Input–Output Representation of Chua's Memristor Circuit
2.3.2 Canonical Input–Output Representation of Murali–Lakshmanan–Chua's Memristor Circuit
2.4 Circuit Implementation of the Canonical Input–Output Representation
2.4.1 The Case of Chua's Memristor Circuit
2.4.2 The Case of Murali–Lakshmanan–Chua's Memristor Circuit
2.5 Numerical Examples
2.5.1 Chua's Memristor Circuit
2.5.2 Murali–Lakshmanan–Chua's Memristor Circuit
2.6 Conclusion
References
3 Energy-Based Memristor Networks for Pattern Recognition in Vision Systems
3.1 Introduction
3.2 Energy-Based Neural Networks
3.2.1 Equilibrium Propagation
3.3 Memristor-Based Recurrent Neural Network
3.4 Simulations
3.5 Conclusions
References
4 Tunable Chaos in Memristor Circuits for Pattern Recognition Tasks
4.1 Introduction
4.2 Flux-Charge Analysis Method
4.3 The Memristor-Based Chua's Oscillator
4.4 Bio-inspired Analog Computing with a Source of Tunable Chaos
4.4.1 The Architecture
4.4.2 The Training
4.4.3 Simulation Methodology
4.4.4 Simulation Results
4.5 Conclusions
References
5 Pattern Formation in an M-CNN Structure Utilizing a Locally Active NbOx Memristor
5.1 Introduction
5.2 The Locally Active NbOx Memristor
5.2.1 The Model Equations
5.2.2 The AC Equivalent Circuit
5.3 The Single Cell
5.3.1 Stability Analysis
5.3.2 Local Activity Analysis
5.3.3 Parameter Space Analysis
5.4 M-CNN Structure and Pattern Formation
5.5 Conclusion
References
Part II In-Memory Computing
6 In-Memory Computing with Non-volatile Memristor CAM Circuits
6.1 Introduction
6.2 Content-Addressable Memory (CAM) Circuits
6.2.1 Memristor CAM Circuits
6.2.2 Analog Memristor CAM Circuits
6.2.3 CAM-Specific Operation Advantages
6.3 Computing with CAMs
6.3.1 Finite Automata
6.3.2 Associative Computing Systems
6.3.3 Locality Sensitive Hashing
6.3.4 Tree-Based Models
6.4 Conclusion and Outlook
References
7 Memristor-Based In-Memory Computing Architecture for Scientific Computing
7.1 Introduction
7.2 Vector–Matrix Multiplication in Memristor Crossbar Array
7.3 In-Memory Scientific Computing Acceleration
7.3.1 Solving Systems of Linear Equations
7.3.2 Solving Partial Differential Equations
7.4 Discussion and Outlook
7.4.1 Device Level
7.4.2 System Level
7.4.3 Conclusion
References
8 Ta/HfO2-based Memristor and Crossbar Arrays for In-Memory Computing
8.1 Introduction
8.2 Highly Reliable Ta/HfO2 Memristors
8.2.1 Structure, Fabrication, and Electrical Behavior
8.2.2 Switching Mechanism Studies
8.3 1T1R Array: Integration and Operation
8.4 In-Memory Computing for Neural Networks
8.4.1 Multi-layer Perceptron
8.4.2 Convolutional Neural Networks
8.4.3 Long Short-Term Memory Networks
8.4.4 Reinforcement Learning
8.5 In-Memory Computing with Embedded Security
8.6 Conclusions
References
Part III Memristive Devices
9 Ionic Nanoarchitectonics for Artificial Intelligence Devices
9.1 Introduction
9.2 Synaptic Behavior of Gap-Type and Gapless-Type Atomic Switches
9.3 Advanced Gap-Type Atomic Switches Using Molecular Layers
9.4 Ionic Decision-Maker for Reinforcement Learning Using Electromotive Force
9.5 Summary
References
10 Optical Memristors: Review of Switching Mechanisms and New Computing Paradigms
10.1 Introduction
10.2 Memristor Basics
10.3 Optical Memristors and Switching Mechanisms
10.3.1 Barrier Modification, Photogating and Photoconductance Effects
10.3.2 Chemical-Based Approaches
10.3.3 Plasmonic Memristors
10.4 Optical Computing Paradigms and Optical Memristor Applications
10.4.1 Optical Memristor Cellular Nonlinear Networks (OM-CNN) for Ultra-Fast Vision Recognition Applications
10.4.2 Neuromorphic Computing: Light Tuneable STDP Learning
10.5 Conclusion and Outlook
References
Part IV Bioinspired Memristor Computing
11 Memristive Models for the Emulation of Biological Learning
11.1 Introduction
11.2 Learning Principles in Biology
11.2.1 Synaptic Plasticity
11.2.2 Long-Term Potentiation
11.2.3 Hebb's Learning Rule and Spike Time-Dependent Plasticity
11.3 Memristive Learning Models
11.3.1 Hebbian Learning
11.3.2 Memristive Hebbian Learning Models
11.4 Conclusion and Outlook
References
12 Organic Memristive Devices and Organic Electrochemical Transistors as Promising Elements for Bio-inspired Systems
12.1 The Meaning and the Importance of Bio-inspired Systems
12.1.1 Bio-inspired Computing
12.1.2 Organic Devices in Neuromorphic Computing
12.2 OMD Bio-inspired Applications
12.2.1 OMDs: Structure and Working Principle
12.2.2 OMDs as Logic Gates
12.2.3 OMDs as Synapse Analogues
12.2.4 Networks
12.2.5 Interfacing Real Neurons
12.3 OECT Bio-inspired Applications
12.3.1 OECTs: Structure and Working Mechanism
12.3.2 OECTs in Neuromorphic Applications
12.4 Conclusions and Perspectives
References
Index