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Mem elements for Neuromorphic Circuits with Artificial Intelligence Applications

Mem elements for Neuromorphic Circuits with Artificial Intelligence Applications
  • Author : Christos Volos
  • Publsiher : Academic Press
  • Release : 15 July 2021
  • ISBN : 9780128211847
  • Pages : 500 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKMem elements for Neuromorphic Circuits with Artificial Intelligence Applications

Summary:
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


Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications

Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications
  • Author : Christos Volos,Viet-Thanh Pham
  • Publisher : Academic Press
  • Release : 15 July 2021
GET THIS BOOKMem-elements for Neuromorphic Circuits with Artificial Intelligence Applications

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,

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Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications

Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications
  • Author : Christos Volos,Viet-Thanh Pham
  • Publisher : Academic Press
  • Release : 01 July 2021
GET THIS BOOKMem-elements for Neuromorphic Circuits with Artificial Intelligence Applications

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

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Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications
  • Author : Jordi Suñé
  • Publisher : MDPI
  • Release : 09 April 2020
GET THIS BOOKMemristors for Neuromorphic Circuits and Artificial Intelligence Applications

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

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Memristor and Memristive Neural Networks

Memristor and Memristive Neural Networks
  • Author : Alex James
  • Publisher : BoD – Books on Demand
  • Release : 04 April 2018
GET THIS BOOKMemristor and Memristive Neural Networks

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

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Backstepping Control of Nonlinear Dynamical Systems

Backstepping Control of Nonlinear Dynamical Systems
  • Author : Sundarapandian Vaidyanathan,Ahmad Taher Azar
  • Publisher : Academic Press
  • Release : 15 August 2020
GET THIS BOOKBackstepping Control of Nonlinear Dynamical Systems

Backstepping Control of Nonlinear Dynamical Systems addresses both the fundamentals of backstepping control and advances in the field. The latest techniques explored include ‘active backstepping control’, ‘adaptive backstepping control’, ‘fuzzy backstepping control’ and ‘adaptive fuzzy backstepping control’. The reference book provides numerous simulations using MATLAB and circuit design. These illustrate the main results of theory and applications of backstepping control of nonlinear control systems. Backstepping control encompasses varied aspects of mechanical engineering and has many different applications within the field.

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Advances in Neuromorphic Memristor Science and Applications

Advances in Neuromorphic Memristor Science and Applications
  • Author : Robert Kozma,Robinson E. Pino,Giovanni E. Pazienza
  • Publisher : Springer Science & Business Media
  • Release : 28 June 2012
GET THIS BOOKAdvances in Neuromorphic Memristor Science and Applications

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

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Memristor Computing Systems

Memristor Computing Systems
  • Author : Leon O. Chua,Ronald Tetzlaff,Angela Slavova
  • Publisher : Springer Nature
  • Release : 25 July 2022
GET THIS BOOKMemristor Computing Systems

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

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Efficient Processing of Deep Neural Networks

Efficient Processing of Deep Neural Networks
  • Author : Vivienne Sze,Yu-Hsin Chen,Tien-Ju Yang,Joel S. Emer
  • Publisher : Morgan & Claypool Publishers
  • Release : 24 June 2020
GET THIS BOOKEfficient Processing of Deep Neural Networks

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or

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Memristor Networks

Memristor Networks
  • Author : Andrew Adamatzky,Leon Chua
  • Publisher : Springer Science & Business Media
  • Release : 18 December 2013
GET THIS BOOKMemristor Networks

Using memristors one can achieve circuit functionalities that are not possible to establish with resistors, capacitors and inductors, therefore the memristor is of great pragmatic usefulness. Potential unique applications of memristors are in spintronic devices, ultra-dense information storage, neuromorphic circuits and programmable electronics. Memristor Networks focuses on the design, fabrication, modelling of and implementation of computation in spatially extended discrete media with many memristors. Top experts in computer science, mathematics, electronics, physics and computer engineering present foundations of the memristor

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Springer Handbook of Computational Intelligence

Springer Handbook of Computational Intelligence
  • Author : Janusz Kacprzyk,Witold Pedrycz
  • Publisher : Springer
  • Release : 28 May 2015
GET THIS BOOKSpringer Handbook of Computational Intelligence

The Springer Handbook for Computational Intelligence is the first book covering the basics, the state-of-the-art and important applications of the dynamic and rapidly expanding discipline of computational intelligence. This comprehensive handbook makes readers familiar with a broad spectrum of approaches to solve various problems in science and technology. Possible approaches include, for example, those being inspired by biology, living organisms and animate systems. Content is organized in seven parts: foundations; fuzzy logic; rough sets; evolutionary computation; neural networks; swarm intelligence

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Evolving Nano-scale Associative Memories with Memristors

Evolving Nano-scale Associative Memories with Memristors
  • Author : Arpita Sinha
  • Publisher : Anonim
  • Release : 28 June 2022
GET THIS BOOKEvolving 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. 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,

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Deep Learning for Computer Architects

Deep Learning for Computer Architects
  • Author : Brandon Reagen,Robert Adolf,Paul Whatmough,Gu-Yeon Wei,David Brooks
  • Publisher : Morgan & Claypool Publishers
  • Release : 22 August 2017
GET THIS BOOKDeep Learning for Computer Architects

This is a primer written for computer architects in the new and rapidly evolving field of deep learning. It reviews how machine learning has evolved since its inception in the 1960s and tracks the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification

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Robotic Tactile Sensing

Robotic Tactile Sensing
  • Author : Ravinder S. Dahiya,Maurizio Valle
  • Publisher : Springer Science & Business Media
  • Release : 29 July 2012
GET THIS BOOKRobotic Tactile Sensing

Future robots are expected to work closely and interact safely with real-world objects and humans alike. Sense of touch is important in this context, as it helps estimate properties such as shape, texture, hardness, material type and many more; provides action related information, such as slip detection; and helps carrying out actions such as rolling an object between fingers without dropping it. This book presents an in-depth description of the solutions available for gathering tactile data, obtaining aforementioned tactile information

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