Results 11 to 20 of about 1,025,950 (315)
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
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In-memory hyperdimensional computing [PDF]
Nature Electronics, 2020 ISSN:2520 ...
Karuanaratne, Geethan +6 more
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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 +11 more
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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 +4 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
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In-memory computing with emerging memory devices: Status and outlook
APL Machine Learning, 2023 In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or suppress the memory bottleneck, which is the major concern for energy efficiency and latency in modern digital computing. While the IMC concept is simple and promising,
P. Mannocci +6 more
semanticscholar +1 more source
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
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Progress and Benchmark of Spiking Neuron Devices and Circuits
Advanced 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
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Multistate resistive switching behaviors for neuromorphic computing in memristor
Materials Today Advances, 2021 Conventional Von Neumann computing systems encounter increasing challenges in the big-data era due to the constraints by the separated data storage and processing. Resistive random-access memory provides dual functionalities of data storage and computing
B. Sun +7 more
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Mixed-precision in-memory computing [PDF]
Nature Electronics, 2018 As CMOS scaling reaches its technological limits, a radical departure from traditional von Neumann systems, which involve separate processing and memory units, is needed in order to significantly extend the performance of today's computers. In-memory computing is a promising approach in which nanoscale resistive memory devices, organized in a ...
Manuel Le Gallo +8 more
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