Results 61 to 70 of about 14,001 (185)

LiNbO3-based memristors for neuromorphic computing applications: a review

Frontiers in Electronic Materials
Neuromorphic computing is a promising paradigm for developing energy-efficient and high-performance artificial intelligence systems. The unique properties of lithium niobate-based (LiNbO3)-based memristors, such as low power consumption, non-volatility ...
Caxton Griffith Kibebe, Yue Liu
doaj   +1 more source

A Survey of Memristive Threshold Logic Circuits

, 2016
In this paper, we review the different memristive threshold logic (MTL) circuits that are inspired from the synaptic action of flow of neurotransmitters in the biological brain.
James, Alex Pappachen, Jayadevi, Deepthi Anirudhan, Maan, Akshay Kumar   +2 more
core   +1 more source

Light-stimulated low-power artificial synapse based on a single GaN nanowire for neuromorphic computing

Photonics Research, 2023
The fast development of the brain-inspired neuromorphic computing system has ignited an urgent demand for artificial synapses with low power consumption. In this work, it is the first time a light-stimulated low-power synaptic device based on a single GaN nanowire has been demonstrated successfully.
Min Zhou, Yukun Zhao, Xiushuo Gu, Qianyi Zhang, Jianya Zhang, Min Jiang, Shulong Lu   +6 more
openaire   +1 more source

Multifunctional Organic Materials, Devices, and Mechanisms for Neuroscience, Neuromorphic Computing, and Bioelectronics

Nano-Micro Letters
Highlights The review emphasizes the switching mechanisms of organic neuromorphic materials. In addition to these switching mechanisms, the capabilities of organic neuromorphic materials in tunable, conformable, and low-power applications, e.g ...
Felix L. Hoch, Qishen Wang, Kian-Guan Lim, Desmond K. Loke   +3 more
doaj   +1 more source

Perspective: Organic electronic materials and devices for neuromorphic engineering

, 2018
Neuromorphic computing and engineering has been the focus of intense research efforts that have been intensified recently by the mutation of Information and Communication Technologies (ICT). In fact, new computing solutions and new hardware platforms are
Alibart, Fabien, Pecqueur, Sebastien, Vuillaume, Dominique   +2 more
core   +2 more sources

Neuromorphic Computing with Memcapacitors: Advancements, Challenges, and Future Directions

Advanced Electronic Materials
Modern applications demand immense data processing and computational power, yet conventional architectures, constrained by the Von Neumann bottleneck and data presentation, struggle to meet these requirements.
Nada AbuHamra, Muhammad Umair Khan, Eman Hassan, Mahmoud Al Qutayri, Baker Mohammad   +4 more
doaj   +1 more source

Short-Term Plasticity and Long-Term Potentiation in Magnetic Tunnel Junctions: Towards Volatile Synapses

, 2016
Synaptic memory is considered to be the main element responsible for learning and cognition in humans. Although traditionally non-volatile long-term plasticity changes have been implemented in nanoelectronic synapses for neuromorphic applications, recent
Roy, Kaushik, Sengupta, Abhronil
core   +1 more source

Artificial Perception Built on Memristive System: Visual, Auditory, and Tactile Sensations

Advanced Intelligent Systems, 2020
The widespread implementation and rapid development of autonomous systems pose stringent performance requirements on emerging sensory systems. In addition to the basic sensing requirements, leading sensory systems are required to process data and extract
Xinglong Ji, Xinyu Zhao, Mei Chee Tan, Rong Zhao   +3 more
doaj   +1 more source

Low-dimensional optoelectronic synaptic devices for neuromorphic vision sensors

Materials Futures, 2023
Neuromorphic systems represent a promising avenue for the development of the next generation of artificial intelligence hardware. Machine vision, one of the cores in artificial intelligence, requires system-level support with low power consumption, low ...
Chengzhai Lv, Fanqing Zhang, Chunyang Li, Zhongyi Li, Jing Zhao   +4 more
doaj   +1 more source

Layer-wise synapse optimization for implementing neural networks on general neuromorphic architectures

, 2017
Deep artificial neural networks (ANNs) can represent a wide range of complex functions. Implementing ANNs in Von Neumann computing systems, though, incurs a high energy cost due to the bottleneck created between CPU and memory.
Gupta, Jayesh K, Kochenderfer, Mykel, Mern, John   +2 more
core   +1 more source