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
Hauptverfasser: Shim, Sung Keun, Lee, Keonuk, Han, Janguk, Shin, Dong Hoon, Lee, Soo Hyung, Cheong, Sunwoo, Jang, Yoon Ho, Hwang, Cheol Seong
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container_end_page n/a
container_issue 50
container_start_page
container_title Advanced materials (Weinheim)
container_volume 36
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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source Wiley Online Library Journals Frontfile Complete
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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