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Advances in Memristor Neural Networks - Modeling and Applications最新文献

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Charge-Controlled Memristor Grid for Edge Detection 用于边缘检测的电荷控制记忆电阻网格
Advances in Memristor Neural Networks - Modeling and Applications Pub Date : 2018-10-03 DOI: 10.5772/INTECHOPEN.78610
A. Sarmiento-Reyes, Y. R. Velásquez
{"title":"Charge-Controlled Memristor Grid for Edge Detection","authors":"A. Sarmiento-Reyes, Y. R. Velásquez","doi":"10.5772/INTECHOPEN.78610","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.78610","url":null,"abstract":"Nonlinear resistive grids have been extensively used in the past for achieving image filtering, focused on both smoothing and edge detection, by resorting to the nonlinear constitutive branch relationships of the elements in the array in order to carry out in fact a minimization algorithm. In this chapter, a specially tailored fully analytical chargecontrolled memristor model is introduced and used in a memristive grid in order to handle the edge detection. The performance of the grid has been tested on a set of 500 images (clean and noisy) and shows an excellent agreement with the outcomes produced by humans.","PeriodicalId":435929,"journal":{"name":"Advances in Memristor Neural Networks - Modeling and Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123065464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Memristive Anodic Oxides: Production, Properties and Applications in Neuromorphic Computing 忆阻性阳极氧化物:生产、性质及在神经形态计算中的应用
Advances in Memristor Neural Networks - Modeling and Applications Pub Date : 2018-10-03 DOI: 10.5772/INTECHOPEN.79292
A. Brenna, F. Corinto, Seyedreza Noori, MarcoOrmellese, M. Pedeferri, M. Diamanti
{"title":"Memristive Anodic Oxides: Production, Properties and Applications in Neuromorphic Computing","authors":"A. Brenna, F. Corinto, Seyedreza Noori, MarcoOrmellese, M. Pedeferri, M. Diamanti","doi":"10.5772/INTECHOPEN.79292","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.79292","url":null,"abstract":"Memristive devices generally consist of metal oxide elements with specific structure and chemical composition, which are crucial to obtain the required variability in resistance. This makes the control of oxide properties vital. While CMOS compatible production technologies for metal oxides deposition generally involve physical or chemical deposition pathways, we here describe the possibility of using an electrochemical technique, anodic oxidation, as an alternative route to produce memristive oxides. In fact, anodiza tion allows to form a very large range of oxides on the surface of valve metals, such as titanium, hafnium, niobium and tantalum, whose thickness, structure and functional properties depend on process parameters imposed. These oxides may be of interest to build neural networks based on memristive elements produced by anodic oxidation.","PeriodicalId":435929,"journal":{"name":"Advances in Memristor Neural Networks - Modeling and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129525347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Memristive Systems Based on Two-Dimensional Materials 基于二维材料的记忆系统
Advances in Memristor Neural Networks - Modeling and Applications Pub Date : 2018-10-03 DOI: 10.5772/INTECHOPEN.78973
G. Panin, O. Kapitanova
{"title":"Memristive Systems Based on Two-Dimensional Materials","authors":"G. Panin, O. Kapitanova","doi":"10.5772/INTECHOPEN.78973","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.78973","url":null,"abstract":"The unique electronic and optical properties of newly discovered 2D crystals such as graphene, graphene oxide, molybdenum disulfide, and so on demonstrate the tremen dous potential in creating ultrahigh-density nano- and bioelectronics for innovative image recognition systems, storage and processing of big data. A new type of memristors with a floating photogate based on biocompatible graphene and other 2D crystals with extremely low power consumption and footprint is considered. The photocatalytic oxidation of graphene is proposed as an effective method of creating synapse-like 2D memris - tive devices with photoresistive switching for nonvolatile electronic memory of ultrahigh density. Particular attention is paid to the new concept of the formation of self-assembled nanoscale memristive elements interfacing artificial electronic neural networks. 2D pho tomemristors with a floating photogate exhibit multiple states controlled in a wide range of electromagnetic radiation and can be used for neuromorphic computations, pattern recognition and image processing needed to create artificial intelligence.","PeriodicalId":435929,"journal":{"name":"Advances in Memristor Neural Networks - Modeling and Applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124036044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
The Roadmap to Realize Memristive Three-Dimensional Neuromorphic Computing System 记忆三维神经形态计算系统的实现路线图
Advances in Memristor Neural Networks - Modeling and Applications Pub Date : 2018-10-03 DOI: 10.5772/INTECHOPEN.78986
Hongyu An, Kangjun Bai, Y. Yi
{"title":"The Roadmap to Realize Memristive Three-Dimensional Neuromorphic Computing System","authors":"Hongyu An, Kangjun Bai, Y. Yi","doi":"10.5772/INTECHOPEN.78986","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.78986","url":null,"abstract":"Neuromorphic computing, an emerging non-von Neumann computing mimicking the physical structure and signal processing technique of mammalian brains, potentially achieves the same level of computing and power efficiencies of mammalian brains. This chapter will discuss the state-of-the-art research trend on neuromorphic computing with memristors as electronic synapses. Furthermore, a novel three-dimensional (3D) neuromorphic computing architecture combining memristor and monolithic 3D integration technology would be introduced; such computing architecture has capabilities to reduce the system power consumption, provide high connectivity, resolve the routing congestion issues, and offer the massively parallel data processing. Moreover, the design methodology of applying the capacitance formed by the through-silicon vias (TSVs) to generate a membrane potential in 3D neuromorphic computing system would be discussed in this chapter.","PeriodicalId":435929,"journal":{"name":"Advances in Memristor Neural Networks - Modeling and Applications","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115676797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
Synaptic Behavior in Metal Oxide-Based Memristors 金属氧化物基忆阻器的突触行为
Advances in Memristor Neural Networks - Modeling and Applications Pub Date : 2018-10-03 DOI: 10.5772/INTECHOPEN.78408
P. Hu, Shuxiang Wu, Shu-wei Li
{"title":"Synaptic Behavior in Metal Oxide-Based Memristors","authors":"P. Hu, Shuxiang Wu, Shu-wei Li","doi":"10.5772/INTECHOPEN.78408","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.78408","url":null,"abstract":"With the end of Moore’s law in sight, new computing paradigms are needed to fulfill the increasing demands on data and processing potentials. Inspired by the operation of the human brain, from the dimensionality, energy and underlying functionalities, neuromorphic computing systems that are building upon circuit elements to mimic the neurobiological activities are good concepts to meet the challenge. As an important factor in a neuromorphic computer, electronic synapse has been intensively studied. The utilization of transistors, atomic switches and memristors has been proposed to perform synaptic functions. Memristors, with several unique properties, are exceptional candidates for emulating artificial synapses and thus for building artificial neural networks. In this paper, metal oxide-based memristor synapses are reviewed, from materials, properties, mechanisms, to architecture. The synaptic plasticity and learning rules are described. The electrical switching characteristics of a variety of metal oxide-based memristors are discussed, with a focus on their application as biological synapses.","PeriodicalId":435929,"journal":{"name":"Advances in Memristor Neural Networks - Modeling and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132830222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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