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Memristive Devices and Networks for Brain‐Inspired Computing
Author(s) -
Zhang Teng,
Yang Ke,
Xu Xiaoyan,
Cai Yimao,
Yang Yuchao,
Huang Ru
Publication year - 2019
Publication title -
physica status solidi (rrl) – rapid research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.786
H-Index - 68
eISSN - 1862-6270
pISSN - 1862-6254
DOI - 10.1002/pssr.201900029
Subject(s) - neuromorphic engineering , memristor , computer science , von neumann architecture , computer architecture , crossbar switch , realization (probability) , efficient energy use , artificial neural network , exploit , construct (python library) , artificial intelligence , electronic engineering , engineering , telecommunications , electrical engineering , computer network , computer security , operating system , statistics , mathematics
As the era of big data approaches, conventional digital computers face increasing difficulties in performance and power efficiency due to their von Neumann architecture. As a result, there is recently a tremendous upsurge of investigations on brain‐inspired neuromorphic hardware with high parallelism and improved efficiency. Memristors are considered as promising building blocks for the realization of artificial synapses and neurons and can therefore be utilized to construct hardware neural networks. Here, a review is provided on existing approaches for the implementation of artificial synapses and neurons based on memristive devices; and the respective advantages and disadvantages of these approaches are evaluated. This is followed by a discussion of hardware accelerators and neuromorphic computing systems that exploit the parallel, in‐memory and analog characteristics of memristive crossbar arrays as well as the intrinsic dynamics of memristors. Finally, the outstanding challenges are addressed that have not yet been resolved in the present studies, and future advances are discussed that might be needed for building intelligent and energy efficient neuromorphic systems.