Results 21 to 30 of about 14,142 (285)

Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends [PDF]

, 2022
A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence.
Corradi, Federico; id_orcid, M. Lakshmi Varshika, Corradi, Federico, Anup Das, Das, Anup Kumar, Lakshmi Varshika, M., Federico Corradi   +6 more
core   +1 more source

Neuromorphic Computing

The Electrochemical Society Interface, 2023
Neuromorphic computing is a rapidly emerging field that seeks to emulate the computational principles of the brain using novel materials and devices. While traditional computing architectures, such as the von Neumann architecture, have experienced exponential improvements in computational power due to the continuous shrinkage of transistor technology ...
  +4 more sources

Editorial: Focus on algorithms for neuromorphic computing

Neuromorphic Computing and Engineering, 2023
Neuromorphic computing provides a promising energy-efficient alternative to von-Neumann-type computing and learning architectures. However, the best neuromorphic hardware is useless without suitable inference and learning algorithms that can fully ...
Robert Legenstein, Arindam Basu, Priyadarshini Panda   +2 more
doaj   +1 more source

A neuromorphic approach to computer vision [PDF]

Communications of the ACM, 2010
Neuroscience is beginning to inspire a new generation of seeing machines.
Serre, Thomas, Poggio, Tomaso A.
openaire   +3 more sources

Neuromorphic Computing

Elements, 2022
In the face of increasingly large computational demands and the impending halt to Moore's law, the semiconductor industry has been forced to re-evaluate the traditional computing paradism. Central to this re-evaluation has been the novel development of neuromorphic computing - an approach that, at its core, seeks to replicate the brain in silicon ...
null Elements, Will Riherd
openaire   +1 more source

LSB2/2022-Synthetic-neuromorphic-computing-in-living-cells: Initial Release

, 2022
Synthetic-neuromorphic-computing-in-living ...
LSB2
core   +1 more source

LSB2/Synthetic-neuromorphic-computing-in-living-cells-_2022:

, 2022
@Synthetic neuromorphic computing in living ...
LSB2
core   +1 more source

Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing

, 2020
Neuro-inspired computing architectures are one of the leading candidates to solve complex and large-scale associative learning problems for AI applications. The two key building blocks for neuromorphic computing are the neuron and the synapse, which form
Linares-Barranco, Bernabé, Avedillo de Juan, María José, Hardelin, Tanguy, Abernot, Madeleine, Moselund, Kirsten, Karachristou, Eirini, Corti, Elisabetta, Gonos, Théophile, Karg, Siegfried, Rousseau, Matthieu, Carapezzi, Stefania, Boudaden, Jamila, Todri-Sanial, Aida, Gil, Thierry, Azemard, Nadine, Serrano-Gotarredona, Teresa, Núñez, Juan, Salles, Jérémie, Nejim, Ahmed, Klumpp, Armin   +19 more
core   +3 more sources

Emerging Opportunities for 2D Materials in Neuromorphic Computing

Nanomaterials, 2023
Recently, two-dimensional (2D) materials and their heterostructures have been recognized as the foundation for future brain-like neuromorphic computing devices.
Chenyin Feng, Wenwei Wu, Huidi Liu, Junke Wang, Houzhao Wan, Guokun Ma, Hao Wang   +6 more
doaj   +1 more source

Low-Power Memristor for Neuromorphic Computing: From Materials to Applications [PDF]

Nano-Micro Letters
Jialin Meng, Tianyu Wang, Wang Zhenlong   +2 more
exaly   +2 more sources