Cargando…
Combinatorial optimization by weight annealing in memristive hopfield networks
The increasing utility of specialized circuits and growing applications of optimization call for the development of efficient hardware accelerator for solving optimization problems. Hopfield neural network is a promising approach for solving combinatorial optimization problems due to the recent demo...
Autores principales: | Fahimi, Z., Mahmoodi, M. R., Nili, H., Polishchuk, Valentin, Strukov, D. B. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8361025/ https://www.ncbi.nlm.nih.gov/pubmed/34385475 http://dx.doi.org/10.1038/s41598-020-78944-5 |
Ejemplares similares
-
Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits
por: Prezioso, M., et al.
Publicado: (2018) -
Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits
por: Bayat, F. Merrikh, et al.
Publicado: (2018) -
Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits
por: Guo, Xinjie, et al.
Publicado: (2015) -
Capacity, Fidelity, and Noise Tolerance of Associative Spatial-Temporal Memories Based on Memristive Neuromorphic Networks
por: Gavrilov, Dmitri, et al.
Publicado: (2018) -
Robust Exponential Memory in Hopfield Networks
por: Hillar, Christopher J., et al.
Publicado: (2018)