Cargando…
Competing memristors for brain-inspired computing
The expeditious development of information technology has led to the rise of artificial intelligence (AI). However, conventional computing systems are prone to volatility, high power consumption, and even delay between the processor and memory, which is referred to as the von Neumann bottleneck, in...
Autores principales: | Kim, Seung Ju, Kim, Sang Bum, Jang, Ho Won |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7797931/ https://www.ncbi.nlm.nih.gov/pubmed/33458606 http://dx.doi.org/10.1016/j.isci.2020.101889 |
Ejemplares similares
-
Memristor-based analogue computing for brain-inspired sound localization with in situ training
por: Gao, Bin, et al.
Publicado: (2022) -
Recent Advances in Cerium Oxide-Based Memristors for Neuromorphic Computing
por: Ali, Sarfraz, et al.
Publicado: (2023) -
Physics inspired compact modelling of [Formula: see text] based memristors
por: Yarragolla, Sahitya, et al.
Publicado: (2022) -
Efficient combinatorial optimization by quantum-inspired parallel annealing in analogue memristor crossbar
por: Jiang, Mingrui, et al.
Publicado: (2023) -
Editorial: Memristor Computing for Neuromorphic Systems
por: Min, Kyeong-Sik, et al.
Publicado: (2021)