Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing
Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligenc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2024-03-01
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| Series: | Small Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/smsc.202300139 |
| _version_ | 1797263267517693952 |
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| author | Shao-Xiang Go Kian-Guan Lim Tae-Hoon Lee Desmond K. Loke |
| author_facet | Shao-Xiang Go Kian-Guan Lim Tae-Hoon Lee Desmond K. Loke |
| author_sort | Shao-Xiang Go |
| collection | DOAJ |
| description | Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized. |
| first_indexed | 2024-04-25T00:10:18Z |
| format | Article |
| id | doaj.art-817020a33c2b4b0e8ddc300f2dd87eb8 |
| institution | Directory Open Access Journal |
| issn | 2688-4046 |
| language | English |
| last_indexed | 2024-04-25T00:10:18Z |
| publishDate | 2024-03-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Small Science |
| spelling | doaj.art-817020a33c2b4b0e8ddc300f2dd87eb82024-03-13T14:27:37ZengWiley-VCHSmall Science2688-40462024-03-0143n/an/a10.1002/smsc.202300139Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor ComputingShao-Xiang Go0Kian-Guan Lim1Tae-Hoon Lee2Desmond K. Loke3Department of Science, Mathematics and Technology Singapore University of Technology and Design Singapore 487372 SingaporeDepartment of Science, Mathematics and Technology Singapore University of Technology and Design Singapore 487372 SingaporeDepartment of Engineering University of Cambridge Trumpington Street Cambridge CB2 1PZ UKDepartment of Science, Mathematics and Technology Singapore University of Technology and Design Singapore 487372 SingaporeSeparate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized.https://doi.org/10.1002/smsc.202300139brain-inspired neuromorphic computingin-memory computingin-sensor computingmolecular dynamics simulationsnonvolatile memristive materialsphysical unclonable functions |
| spellingShingle | Shao-Xiang Go Kian-Guan Lim Tae-Hoon Lee Desmond K. Loke Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing Small Science brain-inspired neuromorphic computing in-memory computing in-sensor computing molecular dynamics simulations nonvolatile memristive materials physical unclonable functions |
| title | Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing |
| title_full | Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing |
| title_fullStr | Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing |
| title_full_unstemmed | Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing |
| title_short | Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing |
| title_sort | nonvolatile memristive materials and physical modeling for in memory and in sensor computing |
| topic | brain-inspired neuromorphic computing in-memory computing in-sensor computing molecular dynamics simulations nonvolatile memristive materials physical unclonable functions |
| url | https://doi.org/10.1002/smsc.202300139 |
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