Results 11 to 20 of about 931,753 (278)
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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Phase-Change Memory for In-Memory Computing [PDF]
Chemical ReviewsIn-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.
Ghazi Sarwat Syed +2 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
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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
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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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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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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 ...
Geethan Karunaratne +5 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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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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