Memristive Devices and Systems: Modelling, Properties & Applications
This reprint presents the Special Issue on “Memristive Devices and Systems: Modeling, Properties, and Applications”. The Special Issue provides a comprehensive overview of key computational primitives enabled by these memory devices, as well as their applications, spanning edge computing, signal pro...
保存先:
| フォーマット: | Online |
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| 言語: | 英語 |
| 出版事項: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
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| 主題: | |
| オンライン･アクセス: | ONIX_20230307_9783036566887_94 |
| タグ: |
タグなし, このレコードへの初めてのタグを付けませんか!
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| _version_ | 1869519629039173632 |
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| collection | Directory of Open Access Books |
| description | This reprint presents the Special Issue on “Memristive Devices and Systems: Modeling, Properties, and Applications”. The Special Issue provides a comprehensive overview of key computational primitives enabled by these memory devices, as well as their applications, spanning edge computing, signal processing, optimization, machine learning, deep learning, stochastic computing, and so on. The memristor is considered to be a promising candidate for next-generation computing systems due to its nonvolatility, high density, low power, nanoscale geometry, nonlinearity, binary/multiple memory capacity, and negative differential resistance. Novel computing architectures/systems based on memristors have shown great potential to replace the traditional von Neumann computing architecture, which faces data movement challenges. With the development of material science, novel preparation and modeling methods for different memristive devices have been put forward recently, which opens up a new path for realizing different computing systems/architectures with practical memristor properties. |
| format | Online |
| id | doab-20.500.12854ir-98084 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-980842024-04-09T23:16:03Z Memristive Devices and Systems: Modelling, Properties & Applications Lai, Chun Sing Dong, Zhekang Qi, Donglian memristor history erase effect dynamic route power-off plot RRAM 1T-1R multilevel compact modeling Verilog-A artificial neural network chaos fractional-order calculus memristor model coexisting attractors Adomian decomposition method VO2 carbon nanotube composite memristor cellular neural network (CNN) von Neumann structure local activity edge of chaos emulator gyrator memcapacitor meminductor memristors memristive systems integrated storage and computation image processing the edge of chaos Hopfield neural network synaptic crosstalk coexisting dynamics optoelectronic memristor composite circuit multi-valued logic rotation mechanism n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology This reprint presents the Special Issue on “Memristive Devices and Systems: Modeling, Properties, and Applications”. The Special Issue provides a comprehensive overview of key computational primitives enabled by these memory devices, as well as their applications, spanning edge computing, signal processing, optimization, machine learning, deep learning, stochastic computing, and so on. The memristor is considered to be a promising candidate for next-generation computing systems due to its nonvolatility, high density, low power, nanoscale geometry, nonlinearity, binary/multiple memory capacity, and negative differential resistance. Novel computing architectures/systems based on memristors have shown great potential to replace the traditional von Neumann computing architecture, which faces data movement challenges. With the development of material science, novel preparation and modeling methods for different memristive devices have been put forward recently, which opens up a new path for realizing different computing systems/architectures with practical memristor properties. 2023-03-07T16:33:16Z 2023-03-07T16:33:16Z 2023 book ONIX_20230307_9783036566887_94 9783036566887 9783036566894 https://directory.doabooks.org/handle/20.500.12854/98084 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/6846 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-6689-4 10.3390/books978-3-0365-6689-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036566887 9783036566894 218 Basel open access |
| spellingShingle | memristor history erase effect dynamic route power-off plot RRAM 1T-1R multilevel compact modeling Verilog-A artificial neural network chaos fractional-order calculus memristor model coexisting attractors Adomian decomposition method VO2 carbon nanotube composite memristor cellular neural network (CNN) von Neumann structure local activity edge of chaos emulator gyrator memcapacitor meminductor memristors memristive systems integrated storage and computation image processing the edge of chaos Hopfield neural network synaptic crosstalk coexisting dynamics optoelectronic memristor composite circuit multi-valued logic rotation mechanism n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Memristive Devices and Systems: Modelling, Properties & Applications |
| title | Memristive Devices and Systems: Modelling, Properties & Applications |
| title_full | Memristive Devices and Systems: Modelling, Properties & Applications |
| title_fullStr | Memristive Devices and Systems: Modelling, Properties & Applications |
| title_full_unstemmed | Memristive Devices and Systems: Modelling, Properties & Applications |
| title_short | Memristive Devices and Systems: Modelling, Properties & Applications |
| title_sort | memristive devices and systems modelling properties applications |
| topic | memristor history erase effect dynamic route power-off plot RRAM 1T-1R multilevel compact modeling Verilog-A artificial neural network chaos fractional-order calculus memristor model coexisting attractors Adomian decomposition method VO2 carbon nanotube composite memristor cellular neural network (CNN) von Neumann structure local activity edge of chaos emulator gyrator memcapacitor meminductor memristors memristive systems integrated storage and computation image processing the edge of chaos Hopfield neural network synaptic crosstalk coexisting dynamics optoelectronic memristor composite circuit multi-valued logic rotation mechanism n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | memristor history erase effect dynamic route power-off plot RRAM 1T-1R multilevel compact modeling Verilog-A artificial neural network chaos fractional-order calculus memristor model coexisting attractors Adomian decomposition method VO2 carbon nanotube composite memristor cellular neural network (CNN) von Neumann structure local activity edge of chaos emulator gyrator memcapacitor meminductor memristors memristive systems integrated storage and computation image processing the edge of chaos Hopfield neural network synaptic crosstalk coexisting dynamics optoelectronic memristor composite circuit multi-valued logic rotation mechanism n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20230307_9783036566887_94 |