Results 31 to 40 of about 48,649 (254)

Transistor-Based Synaptic Devices for Neuromorphic Computing

Crystals
Currently, neuromorphic computing is regarded as the most efficient way to solve the von Neumann bottleneck. Transistor-based devices have been considered suitable for emulating synaptic functions in neuromorphic computing due to their synergistic ...
Wen Huang, Huixing Zhang, Zhengjian Lin, Pengjie Hang, Xing’ao Li   +4 more
doaj   +1 more source

Pseudo-transistors for emerging neuromorphic electronics

Science and Technology of Advanced Materials, 2023
Artificial synaptic devices are the cornerstone of neuromorphic electronics. The development of new artificial synaptic devices and the simulation of biological synaptic computational functions are important tasks in the field of neuromorphic electronics.
Jingwei Fu, Jie Wang, Xiang He, Jianyu Ming, Le Wang, Yiru Wang, He Shao, Chaoyue Zheng, Linghai Xie, Haifeng Ling   +9 more
doaj   +1 more source

Recent Advances of Graphene and Related Materials in Artificial Intelligence

Advanced Intelligent Systems, 2022
Biological brains perform real‐time processing of unstructured data with ultralow energy consumption and represent the most efficient computing systems.
Meirong Huang, Zechen Li, Hongwei Zhu
doaj   +1 more source

Paramagnetic Rim Lesions Are Associated With Trans‐Synaptic Degeneration of the Visual Pathway in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objectives Retrograde trans‐synaptic degeneration (rTSD) from posterior visual pathway lesions in multiple sclerosis (MS) is characterized by hemi‐macular ganglion cell‐inner plexiform layer (GCIPL) thinning and contralateral visual field loss.
Abdul Jaber Tayem, Angel Liu, Sargis Manukyan, Mustafa Subhi, Elaina Luskin, Bryan Quah, Sreekanth Madhusoodhanan Nair, Arzu C. Has Silemek, Gabriela Zabala, Yujie Cui, Laura Locke, Paula Barreras, Marwa Kaisey, Vinicius Calsavara, Daniel S. Reich, Nancy L. Sicotte, Pascal Sati, Omar Al‐Louzi   +17 more
wiley   +1 more source

Modulating short-term and long-term plasticity of polymer-based artificial synapses for neuromorphic computing and beyond

Neuromorphic Computing and Engineering
Neuromorphic devices that emulate biological neural systems have been actively studied to overcome the limitations of conventional von Neumann computing structure.
Ui-Chan Jeong, Jun-Seok Ro, Hea-Lim Park, Tae-Woo Lee   +3 more
doaj   +1 more source

Ultralow Power Wearable Heterosynapse with Photoelectric Synergistic Modulation

Advanced Science, 2020
Although the energy consumption of reported neuromorphic computing devices inspired by biological systems has become lower than traditional memory, it still remains greater than bio‐synapses (≈10 fJ per spike). Herein, a flexible MoS2‐based heterosynapse
Tian‐Yu Wang, Jia‐Lin Meng, Zhen‐Yu He, Lin Chen, Hao Zhu, Qing‐Qing Sun, Shi‐Jin Ding, Peng Zhou, David Wei Zhang   +8 more
doaj   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Intermediate Resistive State in Wafer‐Scale Vertical MoS2 Memristors Through Lateral Silver Filament Growth for Artificial Synapse Applications

Advanced Functional Materials, EarlyView.
In MOCVD MoS2 memristors, a current compliance‐regulated Ag filament mechanism is revealed. The filament ruptures spontaneously during volatile switching, while subsequent growth proceeds vertically through the MoS2 layers and then laterally along the van der Waals gaps during nonvolatile switching.
Yuan Fa, Milan Buttberg, Ke Ran, Rana Walied Ahmad, Dennis Braun, Lukas Völkel, Jimin Lee, Sofía Cruces, Bart Macco, Bárbara Canto, Holger Lerch, Thorsten Wahlbrink, Holger Kalisch, Michael Heuken, Andrei Vescan, Joachim Mayer, Zhenxing Wang, Ilia Valov, Stephan Menzel, Max C. Lemme   +19 more
wiley   +1 more source

On-Chip Trainable Spiking Neural Networks Using Time-To-First-Spike Encoding

IEEE Access, 2022
Artificial Neural Networks (ANNs) have shown remarkable performance in various fields. However, ANN relies on the von-Neumann architecture, which consumes a lot of power. Hardware-based spiking neural networks (SNNs) inspired by a human brain have become
Jiseong Im, Jaehyeon Kim, Ho-Nam Yoo, Jong-Won Baek, Dongseok Kwon, Seongbin Oh, Jangsaeng Kim, Joon Hwang, Byung-Gook Park, Jong-Ho Lee   +9 more
doaj   +1 more source

Optoelectronic Synaptic Devices Using Molecular Telluride Phase‐Change Inks for Three‐Factor Learning

Advanced Functional Materials, EarlyView.
Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner, Dhananjeya Kumaar, Giacomo Torti, Killian Keller, Till Zellweger, Florian M. Schenk, Christoph Weilenmann, Daniel Chelladurai, Vara Prasad Jonnalagadda, Ghazi Sarwat Syed, Vanessa Wood, Jürg Leuthold, Alexandros Emboras, Maksym Yarema, Mathieu Luisier   +14 more
wiley   +1 more source