Memristors for Energy‐Efficient New Computing Paradigms
By Doo Seok Jeong, Keum Do Kim, Sungho Kim, B. Choi, C. S. Hwang. 2016
349 citations recorded by the source corpus.
Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/f89284e559b3f3d9735e2388031a786c6ebf599d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cellular and Molecular Neuroscience
- Daniele Ielmini
- H. Philip Wong
- George G. Malliaras
- Alberto Salleo
- Armantas Melianas
- Scott T. Keene
- Y. van de Burgt
- Shimeng Yu
In Electrical and Electronic Engineering
- Daniele Ielmini
- Abu Sebastian
- M. Le Gallo
- H. Philip Wong
- Evangelos Eleftheriou
- George G. Malliaras
- Alberto Salleo
- Armantas Melianas
Citations in the corpus
- Organic electronics for neuromorphic computing (2018, methodology): “Ultimately, the switching energy should be below 1 pJ to be competitive with traditional complementary metal–oxide–semiconductor (CMOS) logic circuits 23 .”
- In-memory computing with resistive switching devices (2018, methodology): “V logic, logic computation is carried out based on physical laws, such as Ohm’s law and Kirchhoff’s law, except that the comparator function is taken by one of the resistive units in the logic gate, thus enabling true in-memory computation.”
- Memory devices and applications for in-memory computing (2020, background): “NaTuRe NaNoTecHNology This non-volatile accumulative behaviour, in spite of its nonlinear and stochastic nature, can be exploited in several applications, such as training deep neural networks, where the conductance values need to be incrementally modified.”