Results 61 to 70 of about 14,142 (285)

Integrated Field‐Free SOT Domain‐Wall Synapses and MTJ Stochastic Neurons for Hardware Boltzmann Machines

Advanced Functional Materials, EarlyView.
Field‐free spin‐orbit torque domain‐wall synapses integrated with stochastic MTJ neurons enable compact hardware Boltzmann machines. Leveraging intrinsic stochasticity and multi‐level conductance, the system achieves efficient probabilistic learning with high accuracy, demonstrating a scalable spintronic platform for energy‐efficient edge AI.
Aijaz H. Lone, Meng Tang, Camelia Florica, Bin He, Jingkai Xu, Selma Amara, Georgian Melinte, Xixiang Zhang, Gianluca Setti   +8 more
wiley   +1 more source

EU H2020 NEURONN: Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing

, 2020
International audienceNeuro-inspired computing employs technologies that enable brain-inspired computing hardware for more efficient and adaptive intelligent systems.
Linares-Barranco, Bernabé, Avedillo de Juan, María José, Hardelin, Tanguy, Abernot, Madeleine, Karachristou, Eirini, Corti, Elisabetta, Shamsi, Jafar, Gonos, Théophile, Karg, Siegfried, Delacour, Corentin, Jiménez Través, Manuel, Carapezzi, Stefania, Mijalkovic, Slobodan, Boudaden, Jamila, Durieu, Marie, Todri-Sanial, Aida, Gil, Thierry, Azemard, Nadine, Serrano-Gotarredona, Teresa, Núñez, Juan, Salles, Jérémie, Nejim, Ahmed, Klumpp, Armin, Camuñas-Mesa, Luis,   +23 more
core   +1 more source

Review and outlook on synaptic devices and chips for neuromorphic systems

Gongneng cailiao yu qijian xuebao
As the limitations of traditional von Neumann architecture in handling big data and artificial intelligence applications become increasingly apparent, new computing architectures such as Computing-In-Memory (CIM) and neuromorphic computing have gradually
Sai-ke ZHU, Yi ZHAO
doaj   +1 more source

Historical Foundation and Practical Guideline for Ferroelectric Switching Kinetic Studies

Advanced Functional Materials, EarlyView.
The P and U pulses in the conventional PUND measurements are not identical because of the interplay between switching current and the measurement circuit components. This circuit effect can lead to a shift in polarization transients and misinterpreted physics in the switching kinetics.
Yi Liang, Pat Kezer, John T. Heron
wiley   +1 more source

Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip

Nature Communications
By mimicking the neurons and synapses of the human brain and employing spiking neural networks on neuromorphic chips, neuromorphic computing offers a promising energy-efficient machine intelligence.
Man Yao, Ole Richter, Guangshe Zhao, Ning Qiao, Yannan Xing, Dingheng Wang, Tianxiang Hu, Wei Fang, Tugba Demirci, Michele De Marchi, Lei Deng, Tianyi Yan, Carsten Nielsen, Sadique Sheik, Chenxi Wu, Yonghong Tian, Bo Xu, Guoqi Li   +17 more
doaj   +1 more source

Implementing Holographic Reduced Representations for Spiking Neural Networks

IEEE Access
Neuromorphic Computing surpasses conventional von Neumann architectures in terms of energy efficiency, parallelisation, scalability, and stochasticity.
Vidura Sumanasena, Daswin de Silva, Evgeny Osipov, Dmitri A. Rachkovskij, Ross W. Gayler   +4 more
doaj   +1 more source

Electro‐Steric Ion Confinement in Polyelectrolyte Networks for Robust Nonvolatile Artificial Synapse

Advanced Functional Materials, EarlyView.
Polyelectrolyte stoichiometry governs ion transport and retention in electrolyte‐gated synaptic transistors. A PSS‐rich network creates electro‐steric ion confinement that suppresses ion back‐diffusion and stabilizes channel doping, enabling robust nonvolatile synaptic memory, linear weight updates, and low‐energy operation.
Donghwa Lee, Jinbo Kim, Myeongjin An, Jisoo Park, Junho Sung, Eunsung Hwang, Suhwan Seok, Seong‐Min Bak, Jeonghun Kim, Eunho Lee   +9 more
wiley   +1 more source

Low‐Power Computing with Neuromorphic Engineering

Advanced Intelligent Systems, 2021
The increasing power consumption in the existing computation architecture presents grand challenges for the performance and reliability of very‐large‐scale integrated circuits. Inspired by the characteristics of the human brain for processing complicated
Dingbang Liu, Hao Yu, Yang Chai
doaj   +1 more source

Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks

Science and Technology of Advanced Materials, 2023
The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data.
Yi Zhang, Zhuohui Huang, Jie Jiang
doaj   +1 more source

An FPGA-based neuromorphic vision system accelerator [PDF]

Rapid reaction to a specific event is a critical feature for an embedded computer vision system to ensure reliable and secure interaction with the environment in resource-limited real-time applications.
Bhowmick, Deepayan, Ali, Teymoor, Lau, Sook Yen, Rainey, James, Appiah, Kofi, Maier, Patrick, Gheorghiu, Elena   +6 more
core   +1 more source