Flash Memory for Synaptic Plasticity in Neuromorphic Computing: A Review
The rapid expansion of data has made global access easier, but it also demands increasing amounts of energy for data storage and processing. In response, neuromorphic electronics, inspired by the functionality of biological neurons and synapses, have emerged as a growing area of research. These devi...
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MDPI AG
2025-02-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/2/121 |
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| author | Jisung Im Sangyeon Pak Sung-Yun Woo Wonjun Shin Sung-Tae Lee |
| author_facet | Jisung Im Sangyeon Pak Sung-Yun Woo Wonjun Shin Sung-Tae Lee |
| author_sort | Jisung Im |
| collection | DOAJ |
| description | The rapid expansion of data has made global access easier, but it also demands increasing amounts of energy for data storage and processing. In response, neuromorphic electronics, inspired by the functionality of biological neurons and synapses, have emerged as a growing area of research. These devices enable in-memory computing, helping to overcome the “von Neumann bottleneck”, a limitation caused by the separation of memory and processing units in traditional von Neumann architecture. By leveraging multi-bit non-volatility, biologically inspired features, and Ohm’s law, synaptic devices show great potential for reducing energy consumption in multiplication and accumulation operations. Within the various non-volatile memory technologies available, flash memory stands out as a highly competitive option for storing large volumes of data. This review highlights recent advancements in neuromorphic computing that utilize NOR, AND, and NAND flash memory. This review also delves into the array architecture, operational methods, and electrical properties of NOR, AND, and NAND flash memory, emphasizing its application in different neural network designs. By providing a detailed overview of flash memory-based neuromorphic computing, this review offers valuable insights into optimizing its use across diverse applications. |
| format | Article |
| id | doaj-art-a2a4b21ccfa9471eac47b64a453b965b |
| institution | DOAJ |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-a2a4b21ccfa9471eac47b64a453b965b2025-08-20T02:44:36ZengMDPI AGBiomimetics2313-76732025-02-0110212110.3390/biomimetics10020121Flash Memory for Synaptic Plasticity in Neuromorphic Computing: A ReviewJisung Im0Sangyeon Pak1Sung-Yun Woo2Wonjun Shin3Sung-Tae Lee4School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of KoreaSchool of Electronic and Electrical Engineering, Hongik University, Seoul 04066, Republic of KoreaSchool of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of KoreaDepartment of Semiconductor Convergence Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaSchool of Electronic and Electrical Engineering, Hongik University, Seoul 04066, Republic of KoreaThe rapid expansion of data has made global access easier, but it also demands increasing amounts of energy for data storage and processing. In response, neuromorphic electronics, inspired by the functionality of biological neurons and synapses, have emerged as a growing area of research. These devices enable in-memory computing, helping to overcome the “von Neumann bottleneck”, a limitation caused by the separation of memory and processing units in traditional von Neumann architecture. By leveraging multi-bit non-volatility, biologically inspired features, and Ohm’s law, synaptic devices show great potential for reducing energy consumption in multiplication and accumulation operations. Within the various non-volatile memory technologies available, flash memory stands out as a highly competitive option for storing large volumes of data. This review highlights recent advancements in neuromorphic computing that utilize NOR, AND, and NAND flash memory. This review also delves into the array architecture, operational methods, and electrical properties of NOR, AND, and NAND flash memory, emphasizing its application in different neural network designs. By providing a detailed overview of flash memory-based neuromorphic computing, this review offers valuable insights into optimizing its use across diverse applications.https://www.mdpi.com/2313-7673/10/2/121neuromorphicsynaptic devicein-memory computingNOR flash memoryAND flash memoryNAND flash memory |
| spellingShingle | Jisung Im Sangyeon Pak Sung-Yun Woo Wonjun Shin Sung-Tae Lee Flash Memory for Synaptic Plasticity in Neuromorphic Computing: A Review Biomimetics neuromorphic synaptic device in-memory computing NOR flash memory AND flash memory NAND flash memory |
| title | Flash Memory for Synaptic Plasticity in Neuromorphic Computing: A Review |
| title_full | Flash Memory for Synaptic Plasticity in Neuromorphic Computing: A Review |
| title_fullStr | Flash Memory for Synaptic Plasticity in Neuromorphic Computing: A Review |
| title_full_unstemmed | Flash Memory for Synaptic Plasticity in Neuromorphic Computing: A Review |
| title_short | Flash Memory for Synaptic Plasticity in Neuromorphic Computing: A Review |
| title_sort | flash memory for synaptic plasticity in neuromorphic computing a review |
| topic | neuromorphic synaptic device in-memory computing NOR flash memory AND flash memory NAND flash memory |
| url | https://www.mdpi.com/2313-7673/10/2/121 |
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