Results 1 to 10 of about 115,019,574 (294)

In-memory mechanical computing

Nature 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   +5 more sources

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

Chem 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

Harnessing the full potential of RRAMs through scalable and distributed in-memory computing with integrated error correction [PDF]

Communications Engineering
Exponential growth in global computing demand is further exacerbated by the high energy requirements of conventional architectures, which are dominated by costly data movement requirements.
Huynh Q. N. Vo, Md Tawsif Rahman Chowdhury, Paritosh Ramanan, Murat Yildirim, Gozde Tutuncuoglu   +4 more
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Memory Optimization Method for Parallel Computing Framework Based on Distributed Dataset [PDF]

Jisuanji 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

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

IEEE 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, Simon Thomann, Girish Pahwa, Yogesh Singh Chauhan, Hussam Amrouch   +4 more
doaj   +1 more source

Compute-in-Memory for Numerical Computations

Micromachines, 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, Yubo Wang, Jin Shao, Yanning Chen, Zhiwang Guo, Cheng Pan, Guangzhi Dong, Min Zhou, Fengxia Wu, Wenhe Wang, Keji Zhou, Xiaoyong Xue   +11 more
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Will computing in memory become a new dawn of associative processors?

Memories - 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

In-memory hyperdimensional computing [PDF]

Nature Electronics, 2020
Hyperdimensional computing (HDC) is an emerging computational framework that takes inspiration from attributes of neuronal circuits such as hyperdimensionality, fully distributed holographic representation, and (pseudo)randomness. When employed for machine learning tasks such as learning and classification, HDC involves manipulation and comparison of ...
Karuanaratne, Geethan, Le Gallo, Manuel, Cherubini, Giovanni, Benini, Luca; id_orcid0000-0001-8068-3806, Rahimi, Abbas; id_orcid0000-0003-3141-4970, Sebastian, Abu, Sebastian, Abu   +6 more
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Memory as a Computational Resource [PDF]

Trends in Cognitive Sciences, 2021
Computer scientists have long recognized that naive implementations of algorithms often result in a paralyzing degree of redundant computation. More sophisticated implementations harness the power of memory by storing computational results and reusing them later.
Ishita, Dasgupta, Samuel J, Gershman
openaire   +2 more sources

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

Advanced 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