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<p>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.</p>

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Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月9日

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  • Younghyun Lee,
  • Hakseung Rhee,
  • Geunyoung Kim,
  • Woon Hyung Cheong,
  • Do Hoon Kim,
  • Hanchan Song,
  • Sooyeon Narie Kay,
  • Jongwon Lee,
  • Kyung Min Kim

摘要

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.