Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis
Abstract Edge computing devices, which generate, collect, process, and analyze data near the source, enhance the data processing efficiency and improve the responsiveness in real-time applications or unstable network environments. To be utilized in wearable and skin-attached electronics, these edge...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59589-2 |
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| author | Younghyun Lee Hakseung Rhee Geunyoung Kim Woon Hyung Cheong Do Hoon Kim Hanchan Song Sooyeon Narie Kay Jongwon Lee Kyung Min Kim |
| author_facet | Younghyun Lee Hakseung Rhee Geunyoung Kim Woon Hyung Cheong Do Hoon Kim Hanchan Song Sooyeon Narie Kay Jongwon Lee Kyung Min Kim |
| author_sort | Younghyun Lee |
| collection | DOAJ |
| description | Abstract Edge computing devices, which generate, collect, process, and analyze data near the source, enhance the data processing efficiency and improve the responsiveness in real-time applications or unstable network environments. To be utilized in wearable and skin-attached electronics, these edge devices must be compact, energy efficient for use in low-power environments, and fabricable on soft substrates. Here, we propose a flexible memristive dot product engine (f-MDPE) designed for edge use and demonstrate its feasibility in a real-time electrocardiogram (ECG) monitoring system. The f-MDPE comprises a 32 × 32 crossbar array embodying a low-temperature processed self-rectifying charge trap memristor on a flexible polyimide substrate and exhibits high uniformity and robust electrical and mechanical stability even under 5-mm bending conditions. Then, we design a neural network training algorithm through hardware-aware approaches and conduct real-time edge ECG diagnosis. This approach achieved an ECG classification accuracy of 93.5%, while consuming only 0.3% of the energy compared to digital approaches, highlighting the strong potential of this approach for emerging edge neuromorphic hardware. |
| format | Article |
| id | doaj-art-3b065b29d208413fbe4cf5e2f5faea09 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-3b065b29d208413fbe4cf5e2f5faea092025-08-20T03:09:20ZengNature PortfolioNature Communications2041-17232025-05-0116111210.1038/s41467-025-59589-2Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosisYounghyun Lee0Hakseung Rhee1Geunyoung Kim2Woon Hyung Cheong3Do Hoon Kim4Hanchan Song5Sooyeon Narie Kay6Jongwon Lee7Kyung Min Kim8Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-guDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-guDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-guDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-guDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-guDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-guDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-guDepartment of Semiconductor Convergence, Chungnam National University (CNU), 99 Daehak-ro, Yuseong-guDepartment of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-guAbstract Edge computing devices, which generate, collect, process, and analyze data near the source, enhance the data processing efficiency and improve the responsiveness in real-time applications or unstable network environments. To be utilized in wearable and skin-attached electronics, these edge devices must be compact, energy efficient for use in low-power environments, and fabricable on soft substrates. Here, we propose a flexible memristive dot product engine (f-MDPE) designed for edge use and demonstrate its feasibility in a real-time electrocardiogram (ECG) monitoring system. The f-MDPE comprises a 32 × 32 crossbar array embodying a low-temperature processed self-rectifying charge trap memristor on a flexible polyimide substrate and exhibits high uniformity and robust electrical and mechanical stability even under 5-mm bending conditions. Then, we design a neural network training algorithm through hardware-aware approaches and conduct real-time edge ECG diagnosis. This approach achieved an ECG classification accuracy of 93.5%, while consuming only 0.3% of the energy compared to digital approaches, highlighting the strong potential of this approach for emerging edge neuromorphic hardware.https://doi.org/10.1038/s41467-025-59589-2 |
| spellingShingle | Younghyun Lee Hakseung Rhee Geunyoung Kim Woon Hyung Cheong Do Hoon Kim Hanchan Song Sooyeon Narie Kay Jongwon Lee Kyung Min Kim Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis Nature Communications |
| title | Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis |
| title_full | Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis |
| title_fullStr | Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis |
| title_full_unstemmed | Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis |
| title_short | Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis |
| title_sort | flexible self rectifying synapse array for energy efficient edge multiplication in electrocardiogram diagnosis |
| url | https://doi.org/10.1038/s41467-025-59589-2 |
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