Thresholding Computing with Heterogeneous Integration of Memristive Kernel with Metal‐Oxide‐Semiconductor Capacitor for Temporal Data Analysis (Adv. Mater. 50/2024)
Thresholding Computing The illustration depicts the process of receiving temporal data by an integrated hardware kernel, which then processes the input to extract meaningful information using memristors. The data flow through the hardware is visualized with signals converging onto the kernel, highli...
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Veröffentlicht in: | Advanced materials (Weinheim) 2024-12, Vol.36 (50), p.n/a |
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creator | Shim, Sung Keun Lee, Keonuk Han, Janguk Shin, Dong Hoon Lee, Soo Hyung Cheong, Sunwoo Jang, Yoon Ho Hwang, Cheol Seong |
description | Thresholding Computing
The illustration depicts the process of receiving temporal data by an integrated hardware kernel, which then processes the input to extract meaningful information using memristors. The data flow through the hardware is visualized with signals converging onto the kernel, highlighting the sophisticated interaction between the temporal input and the advanced memory elements, ultimately enabling efficient data extraction and analysis. More details can be found in article number 2410432 by Yoon Ho Jang, Cheol Seong Hwang, and co‐workers. |
doi_str_mv | 10.1002/adma.202470397 |
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The illustration depicts the process of receiving temporal data by an integrated hardware kernel, which then processes the input to extract meaningful information using memristors. The data flow through the hardware is visualized with signals converging onto the kernel, highlighting the sophisticated interaction between the temporal input and the advanced memory elements, ultimately enabling efficient data extraction and analysis. More details can be found in article number 2410432 by Yoon Ho Jang, Cheol Seong Hwang, and co‐workers.</description><subject>analog memristor</subject><subject>Computation</subject><subject>Convergence</subject><subject>Data analysis</subject><subject>Dataflow kernels</subject><subject>event detection</subject><subject>Hardware</subject><subject>heterogenous integration</subject><subject>neuromorphic hardware kernel</subject><subject>thresholding computing</subject><issn>0935-9648</issn><issn>1521-4095</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNqFUbtu3DAQFIIYyMVOm5pAmqSQvNSLYimcndiwDy5yqYUNtbyjIYkKSdm5Lp-Qz8h35Usi4Qy7TLGYKWZ2djFR9J5DwgHSc2x7TFJIcwGZFK-iFS9SHucgi9fRCmRWxLLMqzfRW-_vAUCWUK6iP9u9I7-3XWuGHVvbfpzCwh5N2LMrCuTsjgayk2fXQ6Cdw2DswKxmG-qd8cE8ELshN1B39GwoYPf31--7n6alGb9Sb5Qd2kkF69gaR1RmYXqeLfWjddixCwzI6gG7gzeefazbh4RtcA5PWAHny0-fzqITjZ2nd094Gn37fLldX8W3d1-u1_VtrDgXIq401yDyEqtcKUlCUK6Aa1W1GaHQVBbF9wxRZkS8qnQqUfAKOIFupZJSZ6fRh-Pe0dkfE_nQ3NvJzaf5JuN5lsIck86q5KhSznrvSDejMz26Q8OhWdpoljaa5zZmgzwaHk1Hh_-om_piU794_wF7s5Jy</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Shim, Sung Keun</creator><creator>Lee, Keonuk</creator><creator>Han, Janguk</creator><creator>Shin, Dong Hoon</creator><creator>Lee, Soo Hyung</creator><creator>Cheong, Sunwoo</creator><creator>Jang, Yoon Ho</creator><creator>Hwang, Cheol Seong</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>20241201</creationdate><title>Thresholding Computing with Heterogeneous Integration of Memristive Kernel with Metal‐Oxide‐Semiconductor Capacitor for Temporal Data Analysis (Adv. Mater. 50/2024)</title><author>Shim, Sung Keun ; Lee, Keonuk ; Han, Janguk ; Shin, Dong Hoon ; Lee, Soo Hyung ; Cheong, Sunwoo ; Jang, Yoon Ho ; Hwang, Cheol Seong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1177-8f1f0746a84cc9e77e4c01fc8d3ea7fe655b3aa93ee188f29a71801e0fd9c99f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>analog memristor</topic><topic>Computation</topic><topic>Convergence</topic><topic>Data analysis</topic><topic>Dataflow kernels</topic><topic>event detection</topic><topic>Hardware</topic><topic>heterogenous integration</topic><topic>neuromorphic hardware kernel</topic><topic>thresholding computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shim, Sung Keun</creatorcontrib><creatorcontrib>Lee, Keonuk</creatorcontrib><creatorcontrib>Han, Janguk</creatorcontrib><creatorcontrib>Shin, Dong Hoon</creatorcontrib><creatorcontrib>Lee, Soo Hyung</creatorcontrib><creatorcontrib>Cheong, Sunwoo</creatorcontrib><creatorcontrib>Jang, Yoon Ho</creatorcontrib><creatorcontrib>Hwang, Cheol Seong</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>Advanced materials (Weinheim)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shim, Sung Keun</au><au>Lee, Keonuk</au><au>Han, Janguk</au><au>Shin, Dong Hoon</au><au>Lee, Soo Hyung</au><au>Cheong, Sunwoo</au><au>Jang, Yoon Ho</au><au>Hwang, Cheol Seong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Thresholding Computing with Heterogeneous Integration of Memristive Kernel with Metal‐Oxide‐Semiconductor Capacitor for Temporal Data Analysis (Adv. Mater. 50/2024)</atitle><jtitle>Advanced materials (Weinheim)</jtitle><date>2024-12-01</date><risdate>2024</risdate><volume>36</volume><issue>50</issue><epage>n/a</epage><issn>0935-9648</issn><eissn>1521-4095</eissn><abstract>Thresholding Computing
The illustration depicts the process of receiving temporal data by an integrated hardware kernel, which then processes the input to extract meaningful information using memristors. The data flow through the hardware is visualized with signals converging onto the kernel, highlighting the sophisticated interaction between the temporal input and the advanced memory elements, ultimately enabling efficient data extraction and analysis. More details can be found in article number 2410432 by Yoon Ho Jang, Cheol Seong Hwang, and co‐workers.</abstract><cop>Weinheim</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/adma.202470397</doi><tpages>1</tpages></addata></record> |
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subjects | analog memristor Computation Convergence Data analysis Dataflow kernels event detection Hardware heterogenous integration neuromorphic hardware kernel thresholding computing |
title | Thresholding Computing with Heterogeneous Integration of Memristive Kernel with Metal‐Oxide‐Semiconductor Capacitor for Temporal Data Analysis (Adv. Mater. 50/2024) |
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