256‐level honey memristor‐based in‐memory neuromorphic system
Abstract Promising synaptic behaviour has been exhibited by memristors based on natural organic materials. Such memristor‐based neuromorphic systems offer notable benefits, including environmental sustainability, low production and disposal costs, non‐volatile storage capability, and bio/Complementa...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-09-01
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| Series: | Electronics Letters |
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| Online Access: | https://doi.org/10.1049/ell2.70029 |
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| author | Harshvardhan Uppaluru Zoe Templin Mohammed Rafeeq Khan Md Omar Faruque Feng Zhao Jinhui Wang |
| author_facet | Harshvardhan Uppaluru Zoe Templin Mohammed Rafeeq Khan Md Omar Faruque Feng Zhao Jinhui Wang |
| author_sort | Harshvardhan Uppaluru |
| collection | DOAJ |
| description | Abstract Promising synaptic behaviour has been exhibited by memristors based on natural organic materials. Such memristor‐based neuromorphic systems offer notable benefits, including environmental sustainability, low production and disposal costs, non‐volatile storage capability, and bio/Complementary Metal‐Oxide‐Semiconductor (CMOS) compatibility. Here, a 256‐level honey memristor‐based neuromorphic system is experimentally evaluated for image recognition. In detail, first, 256‐level honey memristors are manufactured and tested based on in‐house technology; next, the non‐linear characteristics and inherent variation of honey memristor devices, which lead to imprecise weight updates and limit the inference accuracy, are investigated. Experimental results indicate that the inference accuracy of the 256‐level honey memristor‐based neuromorphic system is greater than 88% without cycle‐to‐cycle variations and 87% with cycle‐to‐cycle variations for different optimization algorithms. The overall performance of optimization algorithms with and without variation is compared in terms of energy and latency, where the momentum algorithm consistently outperforms the rest of the algorithms. This 256‐level honey memristor is a promising alternative enabling sustainable neuromorphic systems, encouraging further research into natural organic materials for neuromorphic computing. |
| format | Article |
| id | doaj-art-d03b45568d894a9c93f549d8a77ea4e1 |
| institution | OA Journals |
| issn | 0013-5194 1350-911X |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj-art-d03b45568d894a9c93f549d8a77ea4e12025-08-20T02:14:22ZengWileyElectronics Letters0013-51941350-911X2024-09-016017n/an/a10.1049/ell2.70029256‐level honey memristor‐based in‐memory neuromorphic systemHarshvardhan Uppaluru0Zoe Templin1Mohammed Rafeeq Khan2Md Omar Faruque3Feng Zhao4Jinhui Wang5Department of Electrical and Computer EngineeringUniversity of South AlabamaMobileAlabamaUSASchool of Engineering and Computer ScienceWashington State UniversityVancouverWashingtonUSADepartment of Electrical and Computer EngineeringUniversity of South AlabamaMobileAlabamaUSADepartment of Electrical and Computer EngineeringUniversity of South AlabamaMobileAlabamaUSASchool of Engineering and Computer ScienceWashington State UniversityVancouverWashingtonUSADepartment of Electrical and Computer EngineeringUniversity of South AlabamaMobileAlabamaUSAAbstract Promising synaptic behaviour has been exhibited by memristors based on natural organic materials. Such memristor‐based neuromorphic systems offer notable benefits, including environmental sustainability, low production and disposal costs, non‐volatile storage capability, and bio/Complementary Metal‐Oxide‐Semiconductor (CMOS) compatibility. Here, a 256‐level honey memristor‐based neuromorphic system is experimentally evaluated for image recognition. In detail, first, 256‐level honey memristors are manufactured and tested based on in‐house technology; next, the non‐linear characteristics and inherent variation of honey memristor devices, which lead to imprecise weight updates and limit the inference accuracy, are investigated. Experimental results indicate that the inference accuracy of the 256‐level honey memristor‐based neuromorphic system is greater than 88% without cycle‐to‐cycle variations and 87% with cycle‐to‐cycle variations for different optimization algorithms. The overall performance of optimization algorithms with and without variation is compared in terms of energy and latency, where the momentum algorithm consistently outperforms the rest of the algorithms. This 256‐level honey memristor is a promising alternative enabling sustainable neuromorphic systems, encouraging further research into natural organic materials for neuromorphic computing.https://doi.org/10.1049/ell2.70029artificial intelligencememristors |
| spellingShingle | Harshvardhan Uppaluru Zoe Templin Mohammed Rafeeq Khan Md Omar Faruque Feng Zhao Jinhui Wang 256‐level honey memristor‐based in‐memory neuromorphic system Electronics Letters artificial intelligence memristors |
| title | 256‐level honey memristor‐based in‐memory neuromorphic system |
| title_full | 256‐level honey memristor‐based in‐memory neuromorphic system |
| title_fullStr | 256‐level honey memristor‐based in‐memory neuromorphic system |
| title_full_unstemmed | 256‐level honey memristor‐based in‐memory neuromorphic system |
| title_short | 256‐level honey memristor‐based in‐memory neuromorphic system |
| title_sort | 256 level honey memristor based in memory neuromorphic system |
| topic | artificial intelligence memristors |
| url | https://doi.org/10.1049/ell2.70029 |
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