51 research outputs found
Memristors : a journey from material engineering to beyond Von-Neumann computing
Memristors are a promising building block to the next generation of computing systems. Since 2008, when the physical implementation of a memristor was first postulated, the scientific community has shown a growing interest in this emerging technology. Thus, many other memristive devices have been studied, exploring a large variety of materials and properties. Furthermore, in order to support the design of prac-tical applications, models in different abstract levels have been developed. In fact, a substantial effort has been devoted to the development of memristive based applications, which includes high-density nonvolatile memories, digital and analog circuits, as well as bio-inspired computing. In this context, this paper presents a survey, in hopes of summarizing the highlights of the literature in the last decade
Memristive System Based Image Processing Technology: A Review and Perspective
Copyright: © 2021 by the authors. As the acquisition, transmission, storage and conversion of images become more efficient, image data are increasing explosively. At the same time, the limitations of conventional computational processing systems based on the Von Neumann architecture continue to emerge, and thus, improving the efficiency of image processing has become a key issue that has bothered scholars working on images for a long time. Memristors with non-volatile, synapse-like, as well as integrated storage-and-computation properties can be used to build intelligent processing systems that are closer to the structure and function of biological brains. They are also of great significance when constructing new intelligent image processing systems with non-Von Neumann architecture and for achieving the integrated storage and computation of image data. Based on this, this paper analyses the mathematical models of memristors and discusses their applications in conventional image processing based on memristive systems as well as image processing based on memristive neural networks, to investigate the potential of memristive systems in image processing. In addition, recent advances and implications of memristive system-based image processing are presented comprehensively, and its development opportunities and challenges in different major areas are explored as well. By establishing a complete spectrum of image processing technologies based on memristive systems, this review attempts to provide a reference for future studies in the field, and it is hoped that scholars can promote its development through interdisciplinary academic exchanges and cooperationNational Natural Science Foundation of China (Grant U1909201, Grant 62001149); Natural Science Foundation of Zhejiang Province (Grant LQ21F010009)
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Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization.
The key operation in stochastic neural networks, which have become the state-of-the-art approach for solving problems in machine learning, information theory, and statistics, is a stochastic dot-product. While there have been many demonstrations of dot-product circuits and, separately, of stochastic neurons, the efficient hardware implementation combining both functionalities is still missing. Here we report compact, fast, energy-efficient, and scalable stochastic dot-product circuits based on either passively integrated metal-oxide memristors or embedded floating-gate memories. The circuit's high performance is due to mixed-signal implementation, while the efficient stochastic operation is achieved by utilizing circuit's noise, intrinsic and/or extrinsic to the memory cell array. The dynamic scaling of weights, enabled by analog memory devices, allows for efficient realization of different annealing approaches to improve functionality. The proposed approach is experimentally verified for two representative applications, namely by implementing neural network for solving a four-node graph-partitioning problem, and a Boltzmann machine with 10-input and 8-hidden neurons
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
Cryogenic Neuromorphic Hardware
The revolution in artificial intelligence (AI) brings up an enormous storage
and data processing requirement. Large power consumption and hardware overhead
have become the main challenges for building next-generation AI hardware. To
mitigate this, Neuromorphic computing has drawn immense attention due to its
excellent capability for data processing with very low power consumption. While
relentless research has been underway for years to minimize the power
consumption in neuromorphic hardware, we are still a long way off from reaching
the energy efficiency of the human brain. Furthermore, design complexity and
process variation hinder the large-scale implementation of current neuromorphic
platforms. Recently, the concept of implementing neuromorphic computing systems
in cryogenic temperature has garnered intense interest thanks to their
excellent speed and power metric. Several cryogenic devices can be engineered
to work as neuromorphic primitives with ultra-low demand for power. Here we
comprehensively review the cryogenic neuromorphic hardware. We classify the
existing cryogenic neuromorphic hardware into several hierarchical categories
and sketch a comparative analysis based on key performance metrics. Our
analysis concisely describes the operation of the associated circuit topology
and outlines the advantages and challenges encountered by the state-of-the-art
technology platforms. Finally, we provide insights to circumvent these
challenges for the future progression of research
2D semiconductor nanomaterials and heterostructures : controlled synthesis and functional applications
Two-dimensional (2D) semiconductors beyond graphene represent the thinnest stable known nanomaterials. Rapid growth of their family and applications during the last decade of the twenty-first century have brought unprecedented opportunities to the advanced nano- and opto-electronic technologies. In this article, we review the latest progress in findings on the developed 2D nanomaterials. Advanced synthesis techniques of these 2D nanomaterials and heterostructures were summarized and their novel applications were discussed. The fabrication techniques include the state-of-the-art developments of the vapor-phase-based deposition methods and novel van der Waals (vdW) exfoliation approaches for fabrication both amorphous and crystalline 2D nanomaterials with a particular focus on the chemical vapor deposition (CVD), atomic layer deposition (ALD) of 2D semiconductors and their heterostructures as well as on vdW exfoliation of 2D surface oxide films of liquid metals
Développement et optimisation au niveau des matériaux des mémoires résistives à changement de valence pour le calcul-en-mémoire
Le dĂ©veloppement des technologies de mĂ©moires rĂ©sistives non-volatiles a permis dâexplorer de nouvelles approches de calcul plus performantes que celles basĂ©es sur lâarchitecture conventionnelle de von Neumann. Notamment, lâapproche de calcul-en-mĂ©moire propose une solution Ă lâĂ©tranglement de von Neumann en poussant lâidĂ©e de concevoir une architecture oĂč il nây a pas de sĂ©paration physique entre le processeur et la mĂ©moire. Cette approche repose sur les propriĂ©tĂ©s uniques des mĂ©moires rĂ©sistives (mĂ©mristors) lorsquâelles sont agencĂ©es en rĂ©seaux crossbar, oĂč les fonctions de sommation et de multiplication sâimplĂ©mentent de maniĂšre naturelle. De plus, la compatibilitĂ© de ces mĂ©moires pour une intĂ©gration avec les technologies CMOS conventionnelles offre des capacitĂ©s agressives de miniaturisation et dâefficacitĂ© Ă©nergĂ©tique.
Pour rĂ©pondre aux exigences de cette intĂ©gration, cette thĂšse a portĂ© dâabord sur le dĂ©veloppement du procĂ©dĂ© de dĂ©pĂŽt du matĂ©riau Ă commutation de rĂ©sistance (TiO2). Lâinfluence de la concentration de dĂ©fauts sur les propriĂ©tĂ©s optiques, structurales et sur la composition chimique du TiO2 a Ă©tĂ© Ă©valuĂ©e.
Par la suite, le matĂ©riau Ă commutation de rĂ©sistance dĂ©veloppĂ© a Ă©tĂ© utilisĂ© pour la fabrication de mĂ©mristors de structure TiN/Al2O3/TiO2-x/Ti/TiN/Al. Le procĂ©dĂ© de fabrication utilisĂ© est compatible CMOS et sâest basĂ© sur le procĂ©dĂ© damascĂšne pour rĂ©duire la rugositĂ© de surface des Ă©lectrodes afin de minimiser la variabilitĂ© entre composants (device-to-device variability).
Les caractĂ©ristiques Ă©lectriques des mĂ©mristors ont Ă©tĂ© Ă©valuĂ©es en quasi-statique ainsi quâen utilisant des courtes impulsions de tension pour reproduire les conditions rĂ©elles dâopĂ©ration. Les propriĂ©tĂ©s de commutation rĂ©sistive analogique ainsi que les fonctions synaptiques de potentialisation et de dĂ©pression Ă long terme ont Ă©tĂ© dĂ©montrĂ©. Les mĂ©mristors fabriquĂ©s peuvent stocker jusquâĂ 3 bits avec une stabilitĂ© temporelle satisfaisante.
Pour rĂ©duire les tensions de forming de nos composants, des stratĂ©gies combinant la modulation de la concentration de dĂ©fauts et lâĂ©paisseur du matĂ©riau actif ainsi quâune Ă©tape de traitement thermique post-dĂ©pĂŽt ont Ă©tĂ© Ă©tudiĂ©es. Cette thĂšse a permis de mettre en oeuvre un procĂ©dĂ© de dĂ©pĂŽt du matĂ©riau Ă commutation de rĂ©sistance, dâĂ©valuer les caractĂ©ristiques Ă©lectriques des mĂ©mristors et leur potentiel Ă implĂ©menter les fonctions synaptiques, ainsi que dâexplorer des stratĂ©gies pertinentes qui peuvent minimiser lâinfluence des tensions de forming sur lâopĂ©ration optimale des rĂ©seau crossbar
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