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Neural Networks: Low‐Power Self‐Rectifying Memristive Artificial Neural Network for Near Internet‐of‐Things Sensor Computing (Adv. Electron. Mater. 6/2021)
Author(s) -
Choi Seok,
Kim Yong,
Van Nguyen Tien,
Jeong Won Hee,
Min KyeongSik,
Choi Byung Joon
Publication year - 2021
Publication title -
advanced electronic materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.25
H-Index - 56
ISSN - 2199-160X
DOI - 10.1002/aelm.202170017
Subject(s) - memristor , internet of things , artificial neural network , crossbar switch , materials science , wireless sensor network , cloud computing , computer science , electrical engineering , nanotechnology , embedded system , computer network , telecommunications , engineering , artificial intelligence , operating system
In article 2100050, Byung Joon Choi and co‐workers report the viability of self‐rectifying memristive artificial neural networks as near IoT‐sensor data processors to reduce the latency and energy consumption from transferring data between the sensor and the cloud server. The low‐power operational memristor crossbar arrays and their image processing ability present an advantage in near IoT sensor computing.