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Nonvolatile Memory Materials for Neuromorphic Intelligent Machines

Title
Nonvolatile Memory Materials for Neuromorphic Intelligent Machines
Author
정두석
Keywords
artificial intelligence; intelligent machine; neuromorphic computing; nonvolatile random access memory
Issue Date
2018-10
Publisher
WILEY-V C H VERLAG GMBH
Citation
ADVANCED MATERIALS, v. 30, no. 42, Article no. 1704729
Abstract
Recent progress in deep learning extends the capability of artificial intelligence to various practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis. While such DNN is virtually built on contemporary data centers of the von Neumann architecture, physical (in part) DNN of non-von Neumann architecture, also known as neuromorphic computing, can remarkably improve learning and inference efficiency. Particularly, resistance-based nonvolatile random access memory (NVRAM) highlights its handy and efficient application to the multiply-accumulate (MAC) operation in an analog manner. Here, an overview is given of the available types of resistance-based NVRAMs and their technological maturity from the material- and device-points of view. Examples within the strategy are subsequently addressed in comparison with their benchmarks (virtual DNN in deep learning). A spiking neural network (SNN) is another type of neural network that is more biologically plausible than the DNN. The successful incorporation of resistance-based NVRAM in SNN-based neuromorphic computing offers an efficient solution to the MAC operation and spike timing-based learning in nature. This strategy is exemplified from a material perspective. Intelligent machines are categorized according to their architecture and learning type. Also, the functionality and usefulness of NVRAM-based neuromorphic computing are addressed.
URI
https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.201704729http://repository.hanyang.ac.kr/handle/20.500.11754/120279
ISSN
0935-9648; 1521-4095
DOI
10.1002/adma.201704729
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > MATERIALS SCIENCE AND ENGINEERING(신소재공학부) > Articles
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