Results 11 to 20 of about 131,454 (265)

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   +3 more sources

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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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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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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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

Graphene Oxide-Based Memristive Logic-in-Memory Circuit Enabling Normally-Off Computing

Nanomaterials, 2023
Memristive logic-in-memory circuits can provide energy- and cost-efficient computing, which is essential for artificial intelligence-based applications in the coming Internet-of-things era.
Yeongkwon Kim, Seung-Bae Jeon, Byung Chul Jang   +2 more
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Architecture of Computing System based on Chiplet

Micromachines, 2022
Computing systems are widely used in medical diagnosis, climate prediction, autonomous vehicles, etc. As the key part of electronics, the performance of computing systems is crucial in the intellectualization of the equipment.
Guangbao Shan, Yanwen Zheng, Chaoyang Xing, Dongdong Chen, Guoliang Li, Yintang Yang   +5 more
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An Efficient and Robust Partial Differential Equation Solver by Flash-Based Computing in Memory

Micromachines, 2023
Flash memory-based computing-in-memory (CIM) architectures have gained popularity due to their remarkable performance in various computation tasks of data processing, including machine learning, neuron networks, and scientific calculations. Especially in
Yueran Qi, Yang Feng, Jixuan Wu, Zhaohui Sun, Maoying Bai, Chengcheng Wang, Hai Wang, Xuepeng Zhan, Junyu Zhang, Jing Liu, Jiezhi Chen   +10 more
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Memory devices and applications for in-memory computing [PDF]

Nature Nanotechnology, 2020
Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one
Abu Sebastian, Manuel Le Gallo, Riduan Khaddam-Aljameh, Evangelos Eleftheriou   +3 more
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Memory-Efficient Fixpoint Computation [PDF]

Formal Methods in System Design, 2020
Abstract Practical adoption of static analysis often requires trading precision for performance. This paper focuses on improving the memory efficiency of abstract interpretation without sacrificing precision or time efficiency. Computationally, abstract interpretation reduces the problem of inferring program invariants to computing a fixpoint
Sung Kook Kim, Arnaud J. Venet, Aditya V. Thakur   +2 more
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