Flexible Self-rectifying Synapse Array for Energy-efficient Edge Multiplication in Electrocardiogram Diagnosis

Flexible Self-rectifying Synapse Array for Energy-efficient Edge Multiplication in Electrocardiogram Diagnosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Flexible Self-rectifying Synapse Array for Energy-efficient Edge Multiplication in Electrocardiogram Diagnosis Kyung Min Kim, Younghyun Lee, Hakseung Rhee, Geun Young Kim, Woon Hyung Cheong, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4575664/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 May, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version 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. Furthermore, our simulations indicated that the energy reduction could be further reduced to 0.001% through device scaling to a 100-nm-line width, highlighting the strong potential of this approach for emerging edge neuromorphic hardware. Physical sciences/Materials science/Materials for devices/Electronic devices Physical sciences/Materials science/Nanoscale materials/Electronic properties and materials Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryfsynapse.docx Supplementaryvideo.mp4 Supplementary Video Cite Share Download PDF Status: Published Journal Publication published 09 May, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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