Beyond von‐Neumann Computing with Nanoscale Phase‐Change Memory Devices

By C. D. Wright, P. Hosseini, J. Diosdado. 2013

322 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/c597572086a2596bdeef196c634b2d15120f359e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Abu Sebastian
2. Daniele Ielmini
3. H. Philip Wong
4. Evangelos Eleftheriou

In Electrical and Electronic Engineering

1. Daniele Ielmini
2. Abu Sebastian
3. M. Le Gallo
4. H. Philip Wong
5. Evangelos Eleftheriou
6. Shimeng Yu
7. Ximeng Guan
8. Ilia Valov

In Materials Chemistry

1. H. Philip Wong
2. Abu Sebastian
3. M. Le Gallo
4. Daniele Ielmini
5. Jie Deng

Citations in the corpus

• In-memory computing with resistive switching devices (2018, background): “Summation can also be extended to the concept of integration of analogue pulses, or spikes, which is an essential feature of integrate-and-fire neurons in spiking neural networks 57,62 .”
• In-memory computing on a photonic platform (2018, unknown): “For a chip with the 1 µ m-long cell of GST, the pump pulse width was varied while keeping a constant power to reach different transmission levels, as shown in Fig.”
• 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.”