Open AccessRecent Progress on Memristive Convolutional Neural Networks for Edge Intelligence
Author(s) -
Qin Yi-Fan,
Bao Han,
Wang Feng,
Chen Jia,
Li Yi,
Miao Xiang-Shui
Publication year - 2020
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202070108
Subject(s) - memristor , convolutional neural network , computer science , enhanced data rates for gsm evolution , realization (probability) , artificial neural network , artificial intelligence , edge device , domain (mathematical analysis) , computer architecture , engineering , electronic engineering , cloud computing , mathematical analysis , statistics , mathematics , operating system
Edge Intelligence In article number 2000114 , Yi Li, Xiang‐Shui Miao, and co‐workers review the recent advances in memristive convolutional neural network (CNN) accelerators for hardware realization of edge intelligence. The compression methods and the combination with the long short term memory (LSTM) neural network show great potential for specific domain applications. Insight of current challenges and outlook of edge intelligence memristor driven CNN accelerators are summarized.