Multi-Layered QCA Content-Addressable Memory Cell Using Low-Power Electronic Interaction for AI-Based Data Learning and Retrieval in Quantum Computing Environment
In this study, we propose a quantum structure of an associative memory cell for effective data learning based on artificial intelligence. For effective learning of related data, content-based retrieval and storage rather than memory address is essential. A content-addressable memory (CAM), which is...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/1/19 |
| _version_ | 1797406365214310400 |
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| author | Jun-Cheol Jeon Amjad Almatrood Hyun-Il Kim |
| author_facet | Jun-Cheol Jeon Amjad Almatrood Hyun-Il Kim |
| author_sort | Jun-Cheol Jeon |
| collection | DOAJ |
| description | In this study, we propose a quantum structure of an associative memory cell for effective data learning based on artificial intelligence. For effective learning of related data, content-based retrieval and storage rather than memory address is essential. A content-addressable memory (CAM), which is an efficient memory cell structure for this purpose, in a quantum computing environment, is designed based on quantum-dot cellular automata (QCA). A CAM cell is composed of a memory unit that stores information, a match unit that performs a search, and a structure, using an XOR gate or an XNOR gate in the match unit, that shows good performance. In this study, we designed an XNOR gate with a multilayer structure based on electron interactions and proposed a QCA-based CAM cell using it. The area and time efficiency are verified through a simulation using QCADesigner, and the quantum cost of the proposed XOR gate and CAM cell were reduced by at least 70% and 15%, respectively, when compared to the latest research. In addition, we physically proved the potential energy owing to the interaction between the electrons inside the QCA cell. We also proposed an additional CAM circuit targeting the reduction in energy dissipation that overcomes the best available designs. The simulation and calculation of power dissipation are performed by QCADesigner-E and it is confirmed that more than 27% is reduced. |
| first_indexed | 2024-03-09T03:24:21Z |
| format | Article |
| id | doaj.art-52c5f92afac34515862721fdb5043932 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T03:24:21Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-52c5f92afac34515862721fdb50439322023-12-03T15:03:27ZengMDPI AGSensors1424-82202022-12-012311910.3390/s23010019Multi-Layered QCA Content-Addressable Memory Cell Using Low-Power Electronic Interaction for AI-Based Data Learning and Retrieval in Quantum Computing EnvironmentJun-Cheol Jeon0Amjad Almatrood1Hyun-Il Kim2Department of Convergence Science, Kongju National University, Gongju 32588, Republic of KoreaDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi ArabiaDepartment of Convergence Science, Kongju National University, Gongju 32588, Republic of KoreaIn this study, we propose a quantum structure of an associative memory cell for effective data learning based on artificial intelligence. For effective learning of related data, content-based retrieval and storage rather than memory address is essential. A content-addressable memory (CAM), which is an efficient memory cell structure for this purpose, in a quantum computing environment, is designed based on quantum-dot cellular automata (QCA). A CAM cell is composed of a memory unit that stores information, a match unit that performs a search, and a structure, using an XOR gate or an XNOR gate in the match unit, that shows good performance. In this study, we designed an XNOR gate with a multilayer structure based on electron interactions and proposed a QCA-based CAM cell using it. The area and time efficiency are verified through a simulation using QCADesigner, and the quantum cost of the proposed XOR gate and CAM cell were reduced by at least 70% and 15%, respectively, when compared to the latest research. In addition, we physically proved the potential energy owing to the interaction between the electrons inside the QCA cell. We also proposed an additional CAM circuit targeting the reduction in energy dissipation that overcomes the best available designs. The simulation and calculation of power dissipation are performed by QCADesigner-E and it is confirmed that more than 27% is reduced.https://www.mdpi.com/1424-8220/23/1/19quantum computingnanotechnologyartificial intelligent learningquantum-dot cellular automatacontent addressable memorylow-power QCA circuits |
| spellingShingle | Jun-Cheol Jeon Amjad Almatrood Hyun-Il Kim Multi-Layered QCA Content-Addressable Memory Cell Using Low-Power Electronic Interaction for AI-Based Data Learning and Retrieval in Quantum Computing Environment Sensors quantum computing nanotechnology artificial intelligent learning quantum-dot cellular automata content addressable memory low-power QCA circuits |
| title | Multi-Layered QCA Content-Addressable Memory Cell Using Low-Power Electronic Interaction for AI-Based Data Learning and Retrieval in Quantum Computing Environment |
| title_full | Multi-Layered QCA Content-Addressable Memory Cell Using Low-Power Electronic Interaction for AI-Based Data Learning and Retrieval in Quantum Computing Environment |
| title_fullStr | Multi-Layered QCA Content-Addressable Memory Cell Using Low-Power Electronic Interaction for AI-Based Data Learning and Retrieval in Quantum Computing Environment |
| title_full_unstemmed | Multi-Layered QCA Content-Addressable Memory Cell Using Low-Power Electronic Interaction for AI-Based Data Learning and Retrieval in Quantum Computing Environment |
| title_short | Multi-Layered QCA Content-Addressable Memory Cell Using Low-Power Electronic Interaction for AI-Based Data Learning and Retrieval in Quantum Computing Environment |
| title_sort | multi layered qca content addressable memory cell using low power electronic interaction for ai based data learning and retrieval in quantum computing environment |
| topic | quantum computing nanotechnology artificial intelligent learning quantum-dot cellular automata content addressable memory low-power QCA circuits |
| url | https://www.mdpi.com/1424-8220/23/1/19 |
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