Results 51 to 60 of about 1,025,950 (315)

In-Memory Computing with Resistive Memory Circuits: Status and Outlook

Electronics, 2021
In-memory computing (IMC) refers to non-von Neumann architectures where data are processed in situ within the memory by taking advantage of physical laws.
G. Pedretti, D. Ielmini
semanticscholar   +1 more source

IN-MEMORY INTELLIGENT COMPUTING

Radio Electronics, Computer Science, Control
Context. Processed big data has social significance for the development of society and industry. Intelligent processing of big data is a condition for creating a collective mind of a social group, company, state and the planet as a whole. At the same time, the economy of big data (Data Economy) takes first place in the evaluation of processing ...
V. I. Hahanov, V. H. Abdullayev, S. V. Chumachenko, E. I. Lytvynova, I. V. Hahanova   +4 more
openaire   +3 more sources

Sub-femto-Joule energy consumption memory device based on van der Waals heterostructure for in-memory computing

Chip, 2022
In-memory computing has carried out calculations in situ within each memory unit and its main power consumption comes from data writing and erasing. Further improvements in the energy efficiency of in-memory computing require memory devices with sub ...
Zi-Jia Su, Zi-Hao Xuan, Jing Liu, Yi Kang, Chun-Sen Liu, Cheng-Jie Zuo   +5 more
doaj   +1 more source

Memory and Parallelism Analysis Using a Platform-Independent Approach

, 2019
Emerging computing architectures such as near-memory computing (NMC) promise improved performance for applications by reducing the data movement between CPU and memory. However, detecting such applications is not a trivial task.
Awan, Ahsan Javed, Corda, Stefano, Corporaal, Henk, Jordans, Roel, Singh, Gagandeep, Stuijk, Sander   +5 more
core   +1 more source

Experimental Demonstration of In‐Memory Computing in a Ferrofluid System

Advances in Materials, 2023
Magnetic fluids are excellent candidates for several important research fields including energy harvesting, biomedical applications, soft robotics, and exploration. However, notwithstanding relevant advancements such as shape reconfigurability, that have
M. Crepaldi, C. Mohan, E. Garofalo, A. Adamatzky, K. Szaciłowski, A. Chiolerio   +5 more
semanticscholar   +1 more source

Convolutional Echo‐State Network with Random Memristors for Spatiotemporal Signal Classification

Advanced Intelligent Systems, 2022
The unprecedented development of Internet of Things results in the explosion of spatiotemporal signals generated by smart edge devices, leading to a surge of interest in real‐time learning of such data.
Shaocong Wang, Hegan Chen, Woyu Zhang, Yi Li, Dingchen Wang, Shuhui Shi, Yaping Zhao, Kam Chi Loong, Xi Chen, Yujiao Dong, Yi Zhang, Yang Jiang, Chaudhry Furqan, Jia Chen, Qing Wang, Xiaoxin Xu, Guangyi Wang, Hongyu Yu, Dashan Shang, Zhongrui Wang   +19 more
doaj   +1 more source

A low-cost parallel implementation of direct numerical simulation of wall turbulence

, 2005
A numerical method for the direct numerical simulation of incompressible wall turbulence in rectangular and cylindrical geometries is presented. The distinctive feature resides in its design being targeted towards an efficient distributed-memory parallel
Bertolotti, del Álamo, Dmitruk, Günther, Iovieno, Jiménez, Kim, Kim, Kwok, Lele, Mahesh, Maurizio Quadrio, Moin, Moser, Na, Paolo Luchini, Pelz, Pozzi, Quadrio, Quadrio, Spotz, Thomas   +21 more
core   +1 more source

Computational role of sleep in memory reorganization

Current Opinion in Neurobiology, 2023
Sleep is considered to play an essential role in memory reorganization. Despite its importance, classical theoretical models did not focus on some sleep characteristics. Here, we review recent theoretical approaches investigating their roles in learning and discuss the possibility that non-rapid eye movement (NREM) sleep selectively consolidates memory,
Yoshida, Kensuke, Toyoizumi, Taro
openaire   +3 more sources

Computing high-degree polynomial gradients in memory [PDF]

Nature Communications
AbstractSpecialized function gradient computing hardware could greatly improve the performance of state-of-the-art optimization algorithms. Prior work on such hardware, performed in the context of Ising Machines and related concepts, is limited to quadratic polynomials and not scalable to commonly used higher-order functions.
Tinish Bhattacharya, George H. Hutchinson, Giacomo Pedretti, Xia Sheng, Jim Ignowski, Thomas Van Vaerenbergh, Ray Beausoleil, John Paul Strachan, Dmitri B. Strukov   +8 more
openaire   +7 more sources

Flash Memory Array for Efficient Implementation of Deep Neural Networks

Advanced Intelligent Systems, 2021
The advancement of artificial intelligence applications is promoted by developing deep neural networks (DNNs) with increasing sizes and putting forward higher computing power requirements of the processing devices.
Runze Han, Yachen Xiang, Peng Huang, Yihao Shan, Xiaoyan Liu, Jinfeng Kang   +5 more
doaj   +1 more source