Mem elements for Neuromorphic Circuits with Artificial Intelligence Applications

Mem elements for Neuromorphic Circuits with Artificial Intelligence Applications

As the last decade has seen an increasing interest in recent advances in mem-elements and their applications in neuromorphic circuits and artificial intelligence, this book will attract researchers in various fields.

Author: Christos Volos

Publisher: Academic Press

ISBN: 0128211849

Category: Architecture

Page: 500

View: 305

Mem-elements for Neuromorphic Circuits and Artificial Intelligence Applications illustrates recent advances and achievements in the field of mem-elements (memristor, memcapacitor, meminductor) and their applications in nonlinear dynamical systems, computer science, analog and digital systems, and especially in neuromorphic circuits and artificial intelligence. This book has a particular focus on mem-elements (memristor, memcapacitor, and meminductor) and their applications. It is mainly devoted to present the most recent results as well as critical aspects and perspectives of on-going research on relevant topics, all of them involving networks of mem-elements devices used in diverse applications. This book contributes to the discussion of memristive materials and transport mechanisms, to present various types of physical structures that can be fabricated to realize mem-elements in integrated circuits and device modeling as an essential aspect of mem-elements research. The last decade the increasing interest of the research community for the recent advances on mem-elements and their applications in neuromorphic circuits and artificial intelligence will attract researchers of various fields. Covers a broad range of interdisciplinary topics between mathematics, circuits, realizations, and practical applications related to nonlinear dynamical systems, nanotechnology, analog and digital systems, computer science and artificial intelligence Presents recent advances in the field of mem-elements (memristor, memcapacitor, meminductor) Includes interesting applications of mem-elements in nonlinear dynamical systems, analog and digital systems, neuromorphic circuits, computer science and artificial intelligence
Categories: Architecture

Mem elements for Neuromorphic Circuits with Artificial Intelligence Applications

Mem elements for Neuromorphic Circuits with Artificial Intelligence Applications

MEM - ELEMENTS FOR NEUROMORPHIC CIRCUITS WITH ARTIFICIAL INTELLIGENCE APPLICATIONS Edited by Christos Volos and Viet - Thanh Pham Mem - elements for Neuromorphic Circuits with Artificial Intelligence Applications illustrates recent ...

Author: Christos Volos

Publisher: Academic Press

ISBN: 9780128232026

Category: Architecture

Page: 568

View: 193

Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications illustrates recent advances in the field of mem-elements (memristor, memcapacitor, meminductor) and their applications in nonlinear dynamical systems, computer science, analog and digital systems, and in neuromorphic circuits and artificial intelligence. The book is mainly devoted to recent results, critical aspects and perspectives of ongoing research on relevant topics, all involving networks of mem-elements devices in diverse applications. Sections contribute to the discussion of memristive materials and transport mechanisms, presenting various types of physical structures that can be fabricated to realize mem-elements in integrated circuits and device modeling. As the last decade has seen an increasing interest in recent advances in mem-elements and their applications in neuromorphic circuits and artificial intelligence, this book will attract researchers in various fields. Covers a broad range of interdisciplinary topics between mathematics, circuits, realizations, and practical applications related to nonlinear dynamical systems, nanotechnology, analog and digital systems, computer science and artificial intelligence Presents recent advances in the field of mem-elements (memristor, memcapacitor, meminductor) Includes interesting applications of mem-elements in nonlinear dynamical systems, analog and digital systems, neuromorphic circuits, computer science and artificial intelligence
Categories: Architecture

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

The proposed memristor HTM circuit was verified by the circuit simulation using memristor's Verilog-A model obtained from the measurement. The proposed crossbar circuit was tested to recognize words and sentences that are composed of ...

Author: Jordi Suñé

Publisher: MDPI

ISBN: 9783039285761

Category: Technology & Engineering

Page: 244

View: 144

Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.
Categories: Technology & Engineering

Advances in Neuromorphic Memristor Science and Applications

Advances in Neuromorphic Memristor Science and Applications

In this experiment, a simple neural network shown in Fig. 3.6 was realized. ... Our work as described above [44] demonstrates the potential of memristive devices for neuromorphic circuits applications. Importantly, it has been recently ...

Author: Robert Kozma

Publisher: Springer Science & Business Media

ISBN: 9789400744912

Category: Medical

Page: 320

View: 535

Physical implementation of the memristor at industrial scale sparked the interest from various disciplines, ranging from physics, nanotechnology, electrical engineering, neuroscience, to intelligent robotics. As any promising new technology, it has raised hopes and questions; it is an extremely challenging task to live up to the high expectations and to devise revolutionary and feasible future applications for memristive devices. The possibility of gathering prominent scientists in the heart of the Silicon Valley given by the 2011 International Joint Conference on Neural Networks held in San Jose, CA, has offered us the unique opportunity of organizing a series of special events on the present status and future perspectives in neuromorphic memristor science. This book presents a selection of the remarkable contributions given by the leaders of the field and it may serve as inspiration and future reference to all researchers that want to explore the extraordinary possibilities given by this revolutionary concept.
Categories: Medical

Memristor and Memristive Neural Networks

Memristor and Memristive Neural Networks

Jo et al. described possible applications in artificial intelligence using memristors as synapses in neuromorphic circuits [15]. Another interesting application is to use memristors for arithmetic/logic operations, such as an adder ...

Author: Alex James

Publisher: BoD – Books on Demand

ISBN: 9789535139478

Category: Computers

Page: 324

View: 702

This book covers a range of models, circuits and systems built with memristor devices and networks in applications to neural networks. It is divided into three parts: (1) Devices, (2) Models and (3) Applications. The resistive switching property is an important aspect of the memristors, and there are several designs of this discussed in this book, such as in metal oxide/organic semiconductor nonvolatile memories, nanoscale switching and degradation of resistive random access memory and graphene oxide-based memristor. The modelling of the memristors is required to ensure that the devices can be put to use and improve emerging application. In this book, various memristor models are discussed, from a mathematical framework to implementations in SPICE and verilog, that will be useful for the practitioners and researchers to get a grounding on the topic. The applications of the memristor models in various neuromorphic networks are discussed covering various neural network models, implementations in A/D converter and hierarchical temporal memories.
Categories: Computers

Memristor Computing Systems

Memristor Computing Systems

The rising of artificial intelligence, Internet of Things (IoT) and cloud computing calls for an ever increasing computing ... The use of emerging nanoscale analog devices as memristors and, more generally, mem-elements, is a long-term ...

Author: Leon O. Chua

Publisher: Springer Nature

ISBN: 9783030905828

Category: Technology & Engineering

Page: 298

View: 907

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.
Categories: Technology & Engineering

Evolving Nano scale Associative Memories with Memristors

Evolving Nano scale Associative Memories with Memristors

Associative Memories (AMs) are essential building blocks for brain-like intelligent computing with applications in artificial vision, speech recognition, artificial intelligence, and robotics.

Author: Arpita Sinha

Publisher:

ISBN: OCLC:801977700

Category: Associative storage

Page: 132

View: 491

Associative Memories (AMs) are essential building blocks for brain-like intelligent computing with applications in artificial vision, speech recognition, artificial intelligence, and robotics. Computations for such applications typically rely on spatial and temporal associations in the input patterns and need to be robust against noise and incomplete patterns. The conventional method for implementing AMs is through Artificial Neural Networks (ANNs). Improving the density of ANN based on conventional circuit elements poses a challenge as devices reach their physical scalability limits. Furthermore, stored information in AMs is vulnerable to destructive input signals. Novel nano-scale components, such as memristors, represent one solution to the density problem. Memristors are non-linear time-dependent circuit elements with an inherently small form factor. However, novel neuromorphic circuits typically use memristors to replace synapses in conventional ANN circuits. This sub-optimal use is primarily because there is no established design methodology to exploit the memristor's non-linear properties in a more encompassing way. The objective of this thesis is to explore denser and more robust AM designs using memristor networks. We hypothesize that such network AMs will be more area-efficient than the traditional ANN designs if we can use the memristor's non-linear property for spatial and time-dependent temporal association. We have built a comprehensive simulation framework that employs Genetic Programming (GP) to evolve AM circuits with memristors. The framework is based on the ParadisEO metaheuristics API and uses ngspice for the circuit evaluation. Our results show that we can evolve efficient memristor-based networks that have the potential to replace conventional ANNs used for AMs. We obtained AMs that a) can learn spatial and temporal correlation in the input patterns; b) optimize the trade-off between the size and the accuracy of the circuits; and c) are robust against destructive noise in the inputs. This robustness was achieved at the expense of additional components in the network. We have shown that automated circuit discovery is a promising tool for memristor-based circuits. Future work will focus on evolving circuits that can be used as a building block for more complicated intelligent computing architectures.
Categories: Associative storage

Memristive Neuromorphics Materials Devices Circuits Architectures Algorithms and their Co Design

Memristive Neuromorphics  Materials  Devices  Circuits  Architectures  Algorithms and their Co Design

Even when used simply as tunable resistors, memristors offer unique opportunities to enhance the performance of conventional data processing systems. Most computing tasks in artificial intelligence (AI) applications consist of ...

Author: Huanglong Li

Publisher: Frontiers Media SA

ISBN: 9782889744602

Category: Science

Page:

View: 578

Categories: Science

Advances and Applications in Nonlinear Control Systems

Advances and Applications in Nonlinear Control Systems

Probably the most interesting of these, is in neuromorphic computing circuits, in which this new element could be used as an artificial synapse. So, in this work, a first step to this approach by studying the effect of using an HP ...

Author: Sundarapandian Vaidyanathan

Publisher: Springer

ISBN: 9783319301693

Category: Technology & Engineering

Page: 683

View: 578

The book reports on the latest advances and applications of nonlinear control systems. It consists of 30 contributed chapters by subject experts who are specialized in the various topics addressed in this book. The special chapters have been brought out in the broad areas of nonlinear control systems such as robotics, nonlinear circuits, power systems, memristors, underwater vehicles, chemical processes, observer design, output regulation, backstepping control, sliding mode control, time-delayed control, variables structure control, robust adaptive control, fuzzy logic control, chaos, hyperchaos, jerk systems, hyperjerk systems, chaos control, chaos synchronization, etc. Special importance was given to chapters offering practical solutions, modeling and novel control methods for the recent research problems in nonlinear control systems. This book will serve as a reference book for graduate students and researchers with a basic knowledge of electrical and control systems engineering. The resulting design procedures on the nonlinear control systems are emphasized using MATLAB software.
Categories: Technology & Engineering

Neuromorphic Devices for Brain inspired Computing

Neuromorphic Devices for Brain inspired Computing

Artificial Intelligence, Perception, and Robotics Qing Wan, Yi Shi ... Additionally, memtransistors have been reported with interesting neuromorphic functions. ... Cramming more components onto integrated circuits. Proc. IEEE.

Author: Qing Wan

Publisher: John Wiley & Sons

ISBN: 9783527349791

Category: Technology & Engineering

Page: 256

View: 741

Explore the cutting-edge of neuromorphic technologies with applications in Artificial Intelligence In Neuromorphic Devices for Brain-Inspired Computing: Artificial Intelligence, Perception, and Robotics, a team of expert engineers delivers a comprehensive discussion of all aspects of neuromorphic electronics designed to assist researchers and professionals to understand and apply all manner of brain-inspired computing and perception technologies. The book covers both memristic and neuromorphic devices, including spintronic, multi-terminal, and neuromorphic perceptual applications. Summarizing recent progress made in five distinct configurations of brain-inspired computing, the authors explore this promising technology’s potential applications in two specific areas: neuromorphic computing systems and neuromorphic perceptual systems. The book also includes: A thorough introduction to two-terminal neuromorphic memristors, including memristive devices and resistive switching mechanisms Comprehensive explorations of spintronic neuromorphic devices and multi-terminal neuromorphic devices with cognitive behaviors Practical discussions of neuromorphic devices based on chalcogenide and organic materials In-depth examinations of neuromorphic computing and perceptual systems with emerging devices Perfect for materials scientists, biochemists, and electronics engineers, Neuromorphic Devices for Brain-Inspired Computing: Artificial Intelligence, Perception, and Robotics will also earn a place in the libraries of neurochemists, neurobiologists, and neurophysiologists.
Categories: Technology & Engineering