Results 101 to 110 of about 2,451 (213)

ReRAM/CMOS Array Integration and Characterization via Design of Experiments

Advanced Electronic Materials
No two fabricated Resistive Random Access Memory (ReRAM) devices are alike. Each device can have its own individual optimal set of operating parameters that gives the best performance.
Imtiaz Hossen, William A. Borders, Advait Madhavan, Shweta Joshi, Patrick M. Braganca, Jabez J. McClelland, Brian D. Hoskins, Gina C. Adam   +7 more
doaj   +1 more source

Direct observation of electronic structure change by resistance random access memory effect in amorphous alumina

AIP Advances, 2019
We measured X-ray absorption spectra of amorphous alumina with vacancy-type oxygen defects (AlOx) which exhibits the resistance random access memory (ReRAM) effect. We were able to detect changes in the electronic structure owing to the ReRAM effect.
Masato Kubota, Seisuke Nigo, Seiichi Kato, Kenta Amemiya   +3 more
doaj   +1 more source

ReFloat: Low-Cost Floating-Point Processing in ReRAM for Accelerating Iterative Linear Solvers

, 2023
Resistive random access memory (ReRAM) is a promising technology that can perform low-cost and in-situ matrix-vector multiplication (MVM) in analog domain. Scientific computing requires high-precision floating-point (FP) processing.
Qian, Xuehai, Song, Linghao, Chen, Fan, Chen, Yiran, Li, Hai   +4 more
core  

Low power memristor-based ReRAM design with Error Correcting Code

, 2012
The emerging memristor-based Resistive RAM (ReRAM) has shown great potential as one of the most promising memory technologies, with the unique properties such as high density, low-power, good-scalability, and non-volatility.
Yang Xiao, Niu, Dimin, Yuan Xie, Xie, Yuan, Xiao, Yang, Dimin Niu   +5 more
core   +1 more source

A thermal-aware optimization framework for ReRAM-based deep neural network acceleration

, 2020
Resistive RAM (ReRAM) is widely regarded as a promising platform for deep neural network (DNN) acceleration. However, the ReRAM device suffers from severe thermal problems that degrade the lifetime and inference accuracy of the ReRAM-based DNN ...
Myeonggu Kang, Shin, Hyein, Kim, Lee-Sup, Hyein Shin, Lee-Sup Kim, Kang, Myeonggu   +5 more
core   +1 more source

Insulator Metal Transition-Based Selector in Crossbar Memory Arrays

Electronic Materials
This article investigates resistive random access memory (ReRAM) crossbar memory arrays, which is a notable development in non-volatile memory technology.
Mahmoud Darwish, László Pohl
doaj   +1 more source

Dynamic Control of ReRam

Resistive Random-Access Memory (ReRAM) is a promising non-volatile memory technology that offers high-speed operation, low power consumption, and high scalability.
Khan, Mohammed Rafeeq, Wang, Jinhui
core  

Single-Event Effect Performance of a Commercial ReRAM

, 2014
We show heavy ion test results of a commercial production-level ReRAM. The memory array is robust to bit upsets.
LaBel, Kenneth, Roche, Nicolas, Buchner, Stephen, Khachatrian, Ani, Phan, Anthony, Kim, Hak S., Chen, Dakai, Wilcox, Edward   +7 more
core  

Purely electronic nanometallic ReRAM

, 2018
Resistance switching random access memory (ReRAM), with the ability to repeatedly modulate electrical resistance, has been highlighted as a feasible high-density memory with the potential to replace negative-AND (NAND) flash memory. Such resistance modulation usually involves ion migration and filament formation, which usually lead to relatively low ...
Lu, Yang, Yoon, Jung Ho, Dong, Yanhao, Chen, I-Wei   +3 more
openaire   +2 more sources

Edge AI with ReRAM-based in-memory computing and ReRAM-aware neural architecture search

Deep neural networks (DNNs) underpin many advanced applications, with performance often scaling with size and complexity. Conventional computing architectures struggle with the demands of DNNs, leading to inefficiencies and higher costs.
Loong, Kam Chi, 龍錦賜
core