Results 21 to 30 of about 85,686 (325)
npj 2D Materials and Applications, 2022 With an increasing demand for artificial intelligence, the emulation of the human brain in neuromorphic computing has led to an extraordinary result in not only simulating synaptic dynamics but also reducing complex circuitry systems and algorithms.
Muhammad Naqi +8 more
doaj +1 more source
Manufacturing of graphene based synaptic devices for optoelectronic applications
International Journal of Extreme Manufacturing, 2023 Neuromorphic computing systems can perform memory and computing tasks in parallel on artificial synaptic devices through simulating synaptic functions, which is promising for breaking the conventional von Neumann bottlenecks at hardware level. Artificial
Kui Zhou +10 more
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Novel synaptic memory device for neuromorphic computing [PDF]
Scientific Reports, 2014 This report discusses the electrical characteristics of two-terminal synaptic memory devices capable of demonstrating an analog change in conductance in response to the varying amplitude and pulse-width of the applied signal. The devices are based on Mn doped HfO₂ material.
MANDAL, S +4 more
openaire +3 more sources
Advanced Science, 2022 Recently, three‐terminal synaptic devices, which separate read and write terminals, have attracted significant attention because they enable nondestructive read‐out and parallel‐access for updating synaptic weights.
Seyong Oh +6 more
doaj +1 more source
Core-Shell Dual-Gate Nanowire Memory as a Synaptic Device for Neuromorphic Application
IEEE Journal of the Electron Devices Society, 2021 In this work, a synaptic device for neuromorphic system is proposed and designed to emulate the biological behaviors in the novel device structure of core-shell dual-gate (CSDG) nanowire flash memory.
Md. Hasan Raza Ansari +3 more
doaj +1 more source
Optoelectronic Synaptic Devices for Neuromorphic Computing [PDF]
Advanced Intelligent Systems, 2020 Neuromorphic computing can potentially solve the von Neumann bottleneck of current mainstream computing because it excels at self‐adaptive learning and highly parallel computing and consumes much less energy. Synaptic devices that mimic biological synapses are critical building blocks for neuromorphic computing.
Yue Wang +7 more
openaire +2 more sources
Synaptic memory devices from CoO/Nb:SrTiO 3 junction [PDF]
Royal Society Open Science, 2019 Non-volatile memristors are promising for future hardware-based neurocomputation application because they are capable of emulating biological synaptic functions. Various material strategies have been studied to pursue better device performance, such as lower energy cost, better biological plausibility, etc.
Le Zhao +5 more
openaire +3 more sources
Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective [PDF]
, 2019 On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential.
Abbott +56 more
core +2 more sources
All oxide based flexible multi-folded invisible synapse as vision photo-receptor
Scientific Reports, 2023 All oxide-based transparent flexible memristor is prioritized for the potential application in artificially simulated biological optoelectronic synaptic devices.
Ping-Xing Chen +2 more
doaj +1 more source
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition [PDF]
, 2015 A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density.
Saxena, Vishal, Wu, Xinyu, Zhu, Kehan
core +3 more sources