TY - JOUR
T1 - Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing
AU - Choi, Sanghyeon
AU - Yang, Jehyeon
AU - Wang, Gunuk
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF-2020M3F3A2A03082825 and NRF-2019R1A2C2003704), KU-KIST Research Fund, Samsung Electronics, and a Korea University Future Research Grant.
Funding Information:
This work was supported by the National Research Foundation of Korea (NRF‐2020M3F3A2A03082825 and NRF‐2019R1A2C2003704), KU‐KIST Research Fund, Samsung Electronics, and a Korea University Future Research Grant.
PY - 2020/12/22
Y1 - 2020/12/22
N2 - Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.
AB - Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.
KW - artificial neural networks
KW - artificial neurons
KW - artificial synapses
KW - memristive electronic devices
KW - memristors
KW - neuromorphic electronics
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U2 - 10.1002/adma.202004659
DO - 10.1002/adma.202004659
M3 - Article
AN - SCOPUS:85091849028
VL - 32
JO - Advanced Materials
JF - Advanced Materials
SN - 0935-9648
IS - 51
M1 - 2004659
ER -