book

Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications

by Christos Volos, Viet-Thanh Pham

June 2021

Intermediate to advanced

568 pages

18h 20m

English

Academic Press

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Abstract1.1. Memristor – the first step1.2. Properties of memristor1.3. Memristive systems1.4. The first physical model of the memristor1.5. Memristor's applications1.6. ConclusionReferences
Abstract2.1. Introduction2.2. Memristor modeling framework2.3. The fingerprints of a memristor2.4. Designing memristor emulators2.5. ConclusionReferences
Abstract3.1. Introduction3.2. 3D, 4D, and 5D memristive systems3.3. Numerical methods3.4. Analysis of memristive-based chaotic oscillators3.5. FPGA implementation of memristive systems3.6. Memristive-based secure communication system3.7. ConclusionsReferences

AbstractAcknowledgements4.1. RF/microwave model of memristive switch and PIN diode4.2. Memristive phase-shifter realization4.3. Reconfigurable dual-band bandpass microwave filter4.4. Dual-band bandpass filter with multilayer dual-mode resonator enhanced with RF memristorReferences
Abstract5.1. Introduction5.2. Chaotic memcapacitor oscillator and its dynamical analysis5.3. Nonlinear feedback control5.4. Chaotic motion extraction from video and ANN5.5. Identification of memcapacitor system with ANN5.6. ConclusionsReferences
AbstractAcknowledgement6.1. Introduction6.2. Model system6.3. Mixed-mode oscillations6.4. Higher dimensional torus and large expanded chaotic attractor6.5. ConclusionReferences
Abstract7.1. Introduction7.2. Memristive two-scroll chaotic system and its basic properties7.3. Dynamical analysis of memristive two-scroll chaotic system7.4. Circuit design and experimental measurements7.5. ConclusionReferences
Abstract8.1. Introduction8.2. Theoretical analysis of an absolute memristor autonomous Van der Pol–Duffing circuit8.3. Electronic implementation of an absolute memristor autonomous Van der Pol–Duffing circuit8.4. ConclusionReferences
Abstract9.1. Introduction9.2. Brief introduction to flux- and charge-controlled memristor models and novel chaotic system9.3. Properties and behaviors of memristor-based novel chaotic system9.4. Projective synchronization between the memristor-based chaotic systems9.5. Simulation results and discussion9.6. Conclusions and future scopeReferences
Abstract10.1. Introduction10.2. Design and analysis of the proposed memristor Helmholtz oscillator10.3. Electronic circuit simulations of the proposed memristor Helmholtz oscillator10.4. Chaos synchronization of unidirectional coupled identical chaotic memristor Helmholtz oscillators10.5. Chaos control of memristor Helmholtz oscillator using single controller10.6. ConclusionReferences
AbstractAcknowledgements11.1. Introduction11.2. Fractional-order calculus preliminaries11.3. Fractional-order memristive systems11.4. Continued fraction expansion (CFE)11.5. Implementation of fractional-order integrators using FPAAs11.6. Electronic implementation of a fractional-order memristive system11.7. ConclusionsReferences
AbstractAcknowledgements12.1. Introduction12.2. Mathematical model of LC network based diode bridge memristor12.3. Memristive Wien-bridge oscillator12.4. Chaotic and periodic bursting oscillations (BOs)12.5. Control of active states and quiescent states in BOs12.6. Control of amplitude in BOs12.7. ConclusionReferences
AbstractAcknowledgement13.1. Introduction13.2. Memristor and mem-systems13.3. Neuromorphic systems13.4. Reservoir computing13.5. ConclusionReferences
Abstract14.1. Introduction14.2. Basic concepts14.3. Non-idealities of memristors14.4. Applications14.5. ConclusionsReferences
AbstractAcknowledgements15.1. Introduction15.2. Brief overview of resistive switching and memristive devices15.3. Materials for resistive switching application15.4. Biomaterial-based memristive devices15.5. Conclusion and future outlookReferences
AbstractAcknowledgements16.1. Introduction16.2. Memristive logic gates in crossbar array16.3. R-R logic gate16.4. Memristive V-R logic16.5. Challenges and outlooksReferences
Abstract17.1. Introduction17.2. Design process17.3. Fabrication process17.4. A practical application example17.5. ConclusionReferences
Abstract18.1. Introduction18.2. Preliminaries18.3. Model description18.4. Template formation18.5. Face recognition18.6. Memristive threshold logic (MTL)18.7. Edge detection with memristive threshold logic (MTL) cells18.8. Circuit realization18.9. Experimental setup18.10. Results and discussion18.11. Future research directions18.12. Conclusion18.13. Key terms and definitionsReferences
AbstractAcknowledgements19.1. Introduction19.2. HfO2-based resistive switching structures19.3. Demonstration of learning rules in memristor devices to mimic biological synapses19.4. Stability and reliability issues of resistive synaptic devices19.5. Physical simulation of memristors19.6. Memristor compact modeling19.7. Memristor random telegraph noise19.8. ConclusionReferences
Abstract20.1. Introduction20.2. What is HTM?20.3. Memristive HTM architectures20.4. Analog backpropagation circuit integration in memristive HTM20.5. Discussion and open problems20.6. ConclusionsReferences
AbstractAcknowledgement21.1. Introduction21.2. Memristor synapse-coupled HNN with three neurons21.3. Bifurcation behaviors with multi-stability21.4. Circuit synthesis and PSIM simulation21.5. ConclusionReferences
Abstract22.1. Introduction22.2. Requirement22.3. Memristive fuzzy logic systems22.4. Delay memristive fuzzy systems22.5. Fractional memristive fuzzy systems22.6. General overview of the area and future trendsReferences
Abstract23.1. Introduction23.2. Model description23.3. Controller design23.4. Numerical results23.5. ConclusionsReferences
Abstract24.1. Introduction24.2. Model of the system and preliminary concepts24.3. Controller design24.4. Simulation results24.5. ConclusionReferences
Abstract25.1. Introduction25.2. Spike domain data processing and learning25.3. Learning in memristive neuromorphic architectures25.4. Open problems and research directions25.5. ConclusionReferences

Content preview from Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications

Index

Activation function, 107–109, 290, 291, 293

Adaptive,

control, 469

control application, 502

controller, 470, 502, 504, 510

synapses, 186

synchronization, 184

Adaptive sliding mode control (ASMC), 184, 486

Analog,

amplifiers, 290

artificial neural network, 287

backpropagation circuit, 431–434

backpropagation learning, 428, 435

circuit, 20, 179, 268, 292, 293, 295, 431, 470

CMOS circuits, 292

computation, 13

conductance behavior, 291

device, 125

domain, 290, 431, 435, 517

domain computation, 427

emulators, 18, 20

filter, 13

hardware, 434, 435

HTM architecture, 436

HTM circuit, 433

implementations, 295

learning, 427

learning circuits, 431, 435

memory, 294, 464

memristive neural networks, 287, 292, 293, 295

memristor, 523

memristor emulator, 30

multiplication circuit, ...

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Publisher Resources

ISBN: 9780128232026