Results 11 to 20 of about 931,340 (223)

In-memory mechanical computing

open access: yesNature Communications, 2023
Mechanical computing requires matter to adapt behavior according to retained knowledge, often through integrated sensing, actuation, and control of deformation.
Tie Mei, Chang Qing Chen
doaj   +3 more sources

Benchmarking In-Memory Computing Architectures

open access: yesIEEE Open Journal of the Solid-State Circuits Society, 2022
In-memory computing (IMC) architectures have emerged as a compelling platform to implement energy-efficient machine learning (ML) systems. However, today, the energy efficiency gains provided by IMC designs seem to be leveling off and it is not clear ...
Naresh R. Shanbhag, Saion K. Roy
doaj   +2 more sources

Phase-Change Memory for In-Memory Computing. [PDF]

open access: yesChem Rev
In-memory computing (IMC) is an emerging computational approach that addresses the processor-memory divide in modern computing systems. The core concept is to leverage the physics of memory devices and their array-level organization to perform computations directly within the memory array.
Syed GS, Le Gallo M, Sebastian A.
europepmc   +3 more sources

Memory Optimization Method for Parallel Computing Framework Based on Distributed Dataset [PDF]

open access: yesJisuanji gongcheng, 2023
With the rapid development of scientific computing and artificial intelligence technology, parallel computing in distributed environment has become an important method for solving large-scale theoretical computing and data processing problems.
XIA Libin, LIU Xiaoyu, JIANG Xiaowei, SUN Gongxing
doaj   +1 more source

In-memory hyperdimensional computing [PDF]

open access: yesNature Electronics, 2020
ISSN:2520 ...
Karuanaratne, Geethan   +6 more
openaire   +4 more sources

Compute-in-Memory for Numerical Computations

open access: yesMicromachines, 2022
In recent years, compute-in-memory (CIM) has been extensively studied to improve the energy efficiency of computing by reducing data movement. At present, CIM is frequently used in data-intensive computing. Data-intensive computing applications, such as all kinds of neural networks (NNs) in machine learning (ML), are regarded as ‘soft’ computing tasks.
Dongyan Zhao   +11 more
openaire   +3 more sources

Cryogenic In-Memory Computing for Quantum Processors Using Commercial 5-nm FinFETs

open access: yesIEEE Open Journal of Circuits and Systems, 2023
Cryogenic CMOS circuits that efficiently connect the classical domain with the quantum world are the cornerstone in bringing large-scale quantum processors to reality. The major challenges are, however, the tight power budget (in the order of milliwatts)
Shivendra Singh Parihar   +4 more
doaj   +1 more source

Will computing in memory become a new dawn of associative processors?

open access: yesMemories - Materials, Devices, Circuits and Systems, 2023
Computer architecture faces an enormous challenge in recent years: while the demand for performance is constantly growing, the performance improvement of general-purpose CPU has almost stalled.
Leonid Yavits
doaj   +1 more source

Device Variation Effects on Neural Network Inference Accuracy in Analog In‐Memory Computing Systems

open access: yesAdvanced Intelligent Systems, 2022
In analog in‐memory computing systems based on nonvolatile memories such as resistive random‐access memory (RRAM), neural network models are often trained offline and then the weights are programmed onto memory devices as conductance values.
Qiwen Wang, Yongmo Park, Wei D. Lu
doaj   +1 more source

Progress and Benchmark of Spiking Neuron Devices and Circuits

open access: yesAdvanced Intelligent Systems, 2021
The sustainability of ever more sophisticated artificial intelligence relies on the continual development of highly energy‐efficient and compact computing hardware that mimics the biological neural networks.
Fu-Xiang Liang   +2 more
doaj   +1 more source

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