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Memristive Devices for Brain-Inspired Computing

Memristive Devices for Brain-Inspired Computing
From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks

by Sabina Spiga,Abu Sebastian,Damien Querlioz,Bipin Rajendran

  • Publisher : Woodhead Publishing
  • Release : 2020-06-12
  • Pages : 564
  • ISBN : 0081027877
  • Language : En, Es, Fr & De
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Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications—Computational Memory, Deep Learning, and Spiking Neural Networks reviews the latest in material and devices engineering for optimizing memristive devices beyond storage applications and toward brain-inspired computing. The book provides readers with an understanding of four key concepts, including materials and device aspects with a view of current materials systems and their remaining barriers, algorithmic aspects comprising basic concepts of neuroscience as well as various computing concepts, the circuits and architectures implementing those algorithms based on memristive technologies, and target applications, including brain-inspired computing, computational memory, and deep learning. This comprehensive book is suitable for an interdisciplinary audience, including materials scientists, physicists, electrical engineers, and computer scientists. Provides readers an overview of four key concepts in this emerging research topic including materials and device aspects, algorithmic aspects, circuits and architectures and target applications Covers a broad range of applications, including brain-inspired computing, computational memory, deep learning and spiking neural networks Includes perspectives from a wide range of disciplines, including materials science, electrical engineering and computing, providing a unique interdisciplinary look at the field

Handbook of Memristor Networks

Handbook of Memristor Networks
A Book

by Leon Chua,Georgios Ch. Sirakoulis,Andrew Adamatzky

  • Publisher : Springer Nature
  • Release : 2019-11-12
  • Pages : 1368
  • ISBN : 331976375X
  • Language : En, Es, Fr & De
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This Handbook presents all aspects of memristor networks in an easy to read and tutorial style. Including many colour illustrations, it covers the foundations of memristor theory and applications, the technology of memristive devices, revised models of the Hodgkin-Huxley Equations and ion channels, neuromorphic architectures, and analyses of the dynamic behaviour of memristive networks. It also shows how to realise computing devices, non-von Neumann architectures and provides future building blocks for deep learning hardware. With contributions from leaders in computer science, mathematics, electronics, physics, material science and engineering, the book offers an indispensable source of information and an inspiring reference text for future generations of computer scientists, mathematicians, physicists, material scientists and engineers working in this dynamic field.

Celebrating the International Year of the Periodic Table: Beyond Mendeleev 150

Celebrating the International Year of the Periodic Table: Beyond Mendeleev 150
A Book

by Mikhail V. Kurushkin,W. H. Eugen Schwarz,Eugene A. Goodilin

  • Publisher : Frontiers Media SA
  • Release : 2021-01-11
  • Pages : 329
  • ISBN : 2889663191
  • Language : En, Es, Fr & De
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Mimicking Synaptic Plasticity and Neural Network Using Memtranstors

Mimicking Synaptic Plasticity and Neural Network Using Memtranstors
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2018
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Abstract: Artificial synaptic devices that mimic the functions of biological synapses have drawn enormous interest because of their potential in developing brain‐inspired computing. Current studies are focusing on memristive devices in which the change of the conductance state is used to emulate synaptic behaviors. Here, a new type of artificial synaptic devices based on the memtranstor is demonstrated, which is a fundamental circuit memelement in addition to the memristor, memcapacitor, and meminductor. The state of transtance (presented by the magnetoelectric voltage) in memtranstors acting as the synaptic weight can be tuned continuously with a large number of nonvolatile levels by engineering the applied voltage pulses. Synaptic behaviors including the long‐term potentiation, long‐term depression, and spiking‐time‐dependent plasticity are implemented in memtranstors made of Ni/0.7Pb(Mg1/3 Nb2/3 )O3 ‐0.3PbTiO3 /Ni multiferroic heterostructures. Simulations reveal the capability of pattern learning in a memtranstor network. The work elucidates the promise of memtranstors as artificial synaptic devices with low energy consumption. Abstract : An artifical synaptic device employing magnetoelectric effects is demonstrated based on memtranstors made of Ni/0.7Pb(Mg1/3 Nb2/3 )O3 –0.3PbTiO3 /Ni multiferroic heterostructures. The memtranstance presented by the magnetoelectric voltage serves as the synaptic weight and is tuned with a large number of nonvolatile levels to mimic the functionality of biological synapses. These results reveal the great potential of memtranstors as artificial synaptic devices with low energy consumption.

Frontiers in Memristive Materials for Neuromorphic Processing Applications

Frontiers in Memristive Materials for Neuromorphic Processing Applications
Proceedings of a Workshop

by National Academies of Sciences Engineering and Medicine,Division on Engineering and Physical Sciences,Board on Physics and Astronomy,Condensed Matter and Materials Research Committee

  • Publisher : Unknown Publisher
  • Release : 2021-09-22
  • Pages : 329
  • ISBN : 9780309683197
  • Language : En, Es, Fr & De
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Current von Neumann style computing is energy inefficient and bandwidth limited as information is physically shuttled via electrons between processor, short term non-volatile memory, and long-term storage. Biologically inspired neuromorphic computing, with its inherent autonomous learning capabilities and much lower power requirements based on analog processing, is seen as an avenue for overcoming these limitations. The development of nanoelectronic memory resistors, or memristors, is essential to neuromorphic architectures as they allow logic-based elements for information processing to be combined directly with nonvolatile memory for efficient emulation of neurons and synapses found in the brain. Memristors are typically composed of a switchable material with nonlinear hysteretic behavior sandwiched between two conducting encoding elements. The design, dynamic control, scaling and fundamental understanding of these materials is essential for establishing memristive devices. To explore the state-of-the-art in the materials fundamentally underlying memristor technologies: their science, their mechanisms and their functional imperatives to realize neuromorphic computing machines, the National Academies of Sciences, Engineering, and Medicine's Board on Physics and Astronomy convened a workshop on February 28, 2020. This publication summarizes the presentation and discussion of the workshop.

Enabling Technologies for Very Large-Scale Synaptic Electronics

Enabling Technologies for Very Large-Scale Synaptic Electronics
A Book

by Themis Prodromakis,Alexantrou Serb

  • Publisher : Frontiers Media SA
  • Release : 2018-07-05
  • Pages : 329
  • ISBN : 2889455084
  • Language : En, Es, Fr & De
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An important part of the colossal effort associated with the understanding of the brain involves using electronics hardware technology in order to reproduce biological behavior in ‘silico’. The idea revolves around leveraging decades of experience in the electronics industry as well as new biological findings that are employed towards reproducing key behaviors of fundamental elements of the brain (notably neurons and synapses) at far greater speed-scale products than any software-only implementation can achieve for the given level of modelling detail. So far, the field of neuromorphic engineering has proven itself as a major source of innovation towards the ‘silicon brain’ goal, with the methods employed by its community largely focused on circuit design (analogue, digital and mixed signal) and standard, commercial, Complementary Metal-Oxide Silicon (CMOS) technology as the preferred `tools of choice’ when trying to simulate or emulate biological behavior. However, alongside the circuit-oriented sector of the community there exists another community developing new electronic technologies with the express aim of creating advanced devices, beyond the capabilities of CMOS, that can intrinsically simulate neuron- or synapse-like behavior. A notable example concerns nanoelectronic devices responding to well-defined input signals by suitably changing their internal state (‘weight’), thereby exhibiting `synapse-like’ plasticity. This is in stark contrast to circuit-oriented approaches where the `synaptic weight’ variable has to be first stored, typically as charge on a capacitor or digitally, and then appropriately changed via complicated circuitry. The shift of very much complexity from circuitry to devices could potentially be a major enabling factor for very-large scale `synaptic electronics’, particularly if the new devices can be operated at much lower power budgets than their corresponding 'traditional' circuit replacements. To bring this promise to fruition, synergy between the well-established practices of the circuit-oriented approach and the vastness of possibilities opened by the advent of novel nanoelectronic devices with rich internal dynamics is absolutely essential and will create the opportunity for radical innovation in both fields. The result of such synergy can be of potentially staggering impact to the progress of our efforts to both simulate the brain and ultimately understand it. In this Research Topic, we wish to provide an overview of what constitutes state-of-the-art in terms of enabling technologies for very large scale synaptic electronics, with particular stress on innovative nanoelectronic devices and circuit/system design techniques that can facilitate the development of very large scale brain-inspired electronic systems

Circuits and Systems for Biomedical Applications

Circuits and Systems for Biomedical Applications
A Book

by Heidari, Hadi,Ghoreishizadeh, Sara

  • Publisher : River Publishers
  • Release : 2018-12-05
  • Pages : 182
  • ISBN : 8770220530
  • Language : En, Es, Fr & De
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Circuits and Systems for Biomedical Applications-UKCAS 2018 covers several advanced topics in the area of Devices, Analog and Mixed-Signal Circuits and Systems for Biomedical Applications. The fundamental aspects of these topics are discussed, and state-of-the-art developments are presented. The book proceeds the 1st United Kingdom Circuits and Systems (UKCAS 2018) Workshop. It addresses multidisciplinary theme areas such as Biosensing, Memristors, next-generation medical diagnostics, neural-inspired circuits, neural implants, neuro-prostheses, prosthetic hand and neuro-rehabilitation. Having perceived the device and circuit assets for such technologies and knowing what challenges these present for the biomedical scientists and engineers, integrated circuits for addressable biosensing are reviewed in the first chapter. The Second Chapter is harnessing the power of the brain using metal­oxide Memristors. The third chapter contains construction of an endoscopic capsule for the diagnostics of dysmotilities in the gastro­intestinal track. The next three chapters are on neural interfaces: analogue building blocks of neural inspired circuits are described in the fourth chapter while chapter five focuses on circuits for bio-potential recording from the brain. Networked Integrated circuits and their use in creating advanced implantable stimulation systems will be discussed in chapter six. This topic will be completed by circuits and systems for control of Prosthetic Hands in seventh chapter and genetically enhanced brain­implants for neuro-rehabilitation in chapter eight.