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.
In the Review@RRL by Yuchao Yang, Ru Huang and co‐workers (article no. http://doi.wiley.com/10.1002/pssr.201900029), existing approaches for the implementation of artificial synapses based on metal ion transport, oxygen ion transport and phase change mechanism, etc. are overviewed, and their advantages and disadvantages are analyzed. This is followed by latest progresses on memristive neurons, ranging from leaky‐integrate‐and‐fire neuron to Hodgkin‐Huxley neuron. From a system perspective, both neural network accelerators that exploit the in‐memory and analog characteristics of memristive arrays as well as neuromorphic systems based on the intrinsic dynamics of memristors are discussed. The authors finally point out the outstanding challenges this field faces and highlight the importance of co‐optimization between devices, circuits and algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.