Results 21 to 30 of about 28,339 (330)

Magnetic Elements for Neuromorphic Computing [PDF]

Molecules, 2020
Neuromorphic computing is assumed to be significantly more energy efficient than, and at the same time expected to outperform, conventional computers in several applications, such as data classification, since it overcomes the so-called von Neumann bottleneck.
Tomasz Blachowicz, Andrea Ehrmann
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, Bartolozzi, Bartolozzi, Benjamin, Bi, Bohte, Detorakis, Faisal, Fouda, Fouda, Furber, Gerstner, Gerstner, Goldberg, Gütig, Hinton, Hopfield, Huayaney, Hyvärinen, Ielmini, Indiveri, Isomura, Jolivet, Katz, Kim, Levy, Li, Lillicrap, Maass, Mead, Merolla, Moreno-Bote, Naous, Neftci, Neftci, Neftci, Paille, Park, Pfister, Prezioso, Puglisi, Qiao, Querlioz, Savin, Saïghi, Schultz, Shouval, Tiago, Urbanczik, Wan, Wang, Williams, Woo, Xie, Yarom, Yu, Yu   +56 more
core   +2 more sources

Polaritonic Neuromorphic Computing Outperforms Linear Classifiers [PDF]

Nano Letters, 2020
Machine learning software applications are nowadays ubiquitous in many fields of science and society for their outstanding capability of solving computationally vast problems like the recognition of patterns and regularities in big datasets. One of the main goals of research is the realization of a physical neural network able to perform data ...
Ballarini Dario, Gianfrate Antonio, Panico Riccardo, Opala Andrzej, Ghosh Sanjib, Dominici Lorenzo, Ardizzone Vincenzo, De Giorgi Milena, Lerario Giovanni, Gigli Giuseppe, Liew Timothy C H, Matuszewski Michal, Sanvitto Daniele   +12 more
openaire   +6 more sources

A Coupled Spintronics Neuromorphic Approach for High‐Performance Reservoir Computing

Advanced Intelligent Systems, 2022
The rapid development in the field of artificial intelligence has increased the demand for neuromorphic computing hardware and its information‐processing capability.
Nozomi Akashi, Yasuo Kuniyoshi, Sumito Tsunegi, Tomohiro Taniguchi, Mitsuhiro Nishida, Ryo Sakurai, Yasumichi Wakao, Kenji Kawashima, Kohei Nakajima   +8 more
doaj   +1 more source

Neuromorphic Photonics Circuits: Contemporary Review

Nanomaterials, 2023
Neuromorphic photonics is a cutting-edge fusion of neuroscience-inspired computing and photonics technology to overcome the constraints of conventional computing architectures. Its significance lies in the potential to transform information processing by
Ruslan V. Kutluyarov, Aida G. Zakoyan, Grigory S. Voronkov, Elizaveta P. Grakhova, Muhammad A. Butt   +4 more
doaj   +1 more source

Nanowire-based synaptic devices for neuromorphic computing

Materials Futures, 2023
The traditional von Neumann structure computers cannot meet the demands of high-speed big data processing; therefore, neuromorphic computing has received a lot of interest in recent years.
Xue Chen, Bingkun Chen, Pengfei Zhao, Vellaisamy A L Roy, Su-Ting Han, Ye Zhou   +5 more
doaj   +1 more source

Emerging memristive neurons for neuromorphic computing and sensing

Science and Technology of Advanced Materials, 2023
Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption.
Zhiyuan Li, Wei Tang, Beining Zhang, Rui Yang, Xiangshui Miao   +4 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

Six networks on a universal neuromorphic computing substrate [PDF]

, 2013
In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and ...
Andreas eGrübl, Daniel eBrüderle, Eric eMüller, Johannes eSchemmel, Karlheinz eMeier, Michael eSchmuker, Michael eSchmuker, Mihai A. ePetrovici, Paul eMüller, Sebastian eJeltsch, Thomas ePfeil   +10 more
core   +3 more sources

Principled neuromorphic reservoir computing [PDF]

Nature Communications
Abstract Reservoir computing advances the intriguing idea that a nonlinear recurrent neural circuit—the reservoir—can encode spatio-temporal input signals to enable efficient ways to perform tasks like classification or regression. However, recently the idea of a monolithic reservoir network that simultaneously buffers input signals and ...
Denis Kleyko, Christopher J. Kymn, Anthony Thomas, Bruno A. Olshausen, Friedrich T. Sommer, E. Paxon Frady   +5 more
openaire   +3 more sources