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Compute-in-Memory for Numerical Computations
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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9144086/ https://www.ncbi.nlm.nih.gov/pubmed/35630198 http://dx.doi.org/10.3390/mi13050731 |
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author | Zhao, Dongyan Wang, Yubo Shao, Jin Chen, Yanning Guo, Zhiwang Pan, Cheng Dong, Guangzhi Zhou, Min Wu, Fengxia Wang, Wenhe Zhou, Keji Xue, Xiaoyong |
author_facet | Zhao, Dongyan Wang, Yubo Shao, Jin Chen, Yanning Guo, Zhiwang Pan, Cheng Dong, Guangzhi Zhou, Min Wu, Fengxia Wang, Wenhe Zhou, Keji Xue, Xiaoyong |
author_sort | Zhao, Dongyan |
collection | PubMed |
description | 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. The ‘soft’ computing tasks are computations that can tolerate low computing precision with little accuracy degradation. However, ‘hard’ tasks aimed at numerical computations require high-precision computing and are also accompanied by energy efficiency problems. Numerical computations exist in lots of applications, including partial differential equations (PDEs) and large-scale matrix multiplication. Therefore, it is necessary to study CIM for numerical computations. This article reviews the recent developments of CIM for numerical computations. The different kinds of numerical methods solving partial differential equations and the transformation of matrixes are deduced in detail. This paper also discusses the iterative computation of a large-scale matrix, which tremendously affects the efficiency of numerical computations. The working procedure of the ReRAM-based partial differential equation solver is emphatically introduced. Moreover, other PDEs solvers, and other research about CIM for numerical computations, are also summarized. Finally, prospects and the future of CIM for numerical computations with high accuracy are discussed. |
format | Online Article Text |
id | pubmed-9144086 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91440862022-05-29 Compute-in-Memory for Numerical Computations Zhao, Dongyan Wang, Yubo Shao, Jin Chen, Yanning Guo, Zhiwang Pan, Cheng Dong, Guangzhi Zhou, Min Wu, Fengxia Wang, Wenhe Zhou, Keji Xue, Xiaoyong Micromachines (Basel) Review 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. The ‘soft’ computing tasks are computations that can tolerate low computing precision with little accuracy degradation. However, ‘hard’ tasks aimed at numerical computations require high-precision computing and are also accompanied by energy efficiency problems. Numerical computations exist in lots of applications, including partial differential equations (PDEs) and large-scale matrix multiplication. Therefore, it is necessary to study CIM for numerical computations. This article reviews the recent developments of CIM for numerical computations. The different kinds of numerical methods solving partial differential equations and the transformation of matrixes are deduced in detail. This paper also discusses the iterative computation of a large-scale matrix, which tremendously affects the efficiency of numerical computations. The working procedure of the ReRAM-based partial differential equation solver is emphatically introduced. Moreover, other PDEs solvers, and other research about CIM for numerical computations, are also summarized. Finally, prospects and the future of CIM for numerical computations with high accuracy are discussed. MDPI 2022-05-02 /pmc/articles/PMC9144086/ /pubmed/35630198 http://dx.doi.org/10.3390/mi13050731 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Zhao, Dongyan Wang, Yubo Shao, Jin Chen, Yanning Guo, Zhiwang Pan, Cheng Dong, Guangzhi Zhou, Min Wu, Fengxia Wang, Wenhe Zhou, Keji Xue, Xiaoyong Compute-in-Memory for Numerical Computations |
title | Compute-in-Memory for Numerical Computations |
title_full | Compute-in-Memory for Numerical Computations |
title_fullStr | Compute-in-Memory for Numerical Computations |
title_full_unstemmed | Compute-in-Memory for Numerical Computations |
title_short | Compute-in-Memory for Numerical Computations |
title_sort | compute-in-memory for numerical computations |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9144086/ https://www.ncbi.nlm.nih.gov/pubmed/35630198 http://dx.doi.org/10.3390/mi13050731 |
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