In-Memory Computing Architectures for Big Data and Machine Learning Applications

Vaclav Snasel; Tran Khanh Dang; Phuong N. H. Pham; Josef Küng; Lingping Kong

doi:10.1007/978-981-19-8069-5_2

In-Memory Computing Architectures for Big Data and Machine Learning Applications

Vaclav Snasel, Tran Khanh Dang, Phuong N. H. Pham, Josef Küng, Lingping Kong

Institute for Application-oriented Knowledge Processing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Traditional computing hardware is working to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial Intelligence algorithms such as Deep Neural Networks and Big Data. In order to get hardware solutions to meet the low-latency and high-throughput computational needs of these algorithms, Non-Von Neumann computing architectures such as In-memory Computing (IMC) have been extensively researched and experimented with over the last five years. This study analyses and reviews works designed to accelerate Machine Learning task. We investigate different architectural aspects and directions and provide our comparative evaluations. We further discuss IMC research’s challenges and limitations and present possible directions.

Original language	English
Title of host publication	Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. 9th International Conference, FDSE 2022, Ho Chi Minh City, Vietnam, November 23–25, 2022, Proceedings
Editors	Tran Khanh Dang, Josef Küng, Tai M. Chung
Publisher	Springer
Pages	19-33
Number of pages	15
Volume	1688
ISBN (Print)	9789811980688
DOIs	https://doi.org/10.1007/978-981-19-8069-5_2
Publication status	Published - 2022

Publication series

Name	Communications in Computer and Information Science
Volume	1688 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Fields of science

102010 Database systems
102015 Information systems
102019 Machine learning

JKU Focus areas

Digital Transformation

Access to Document

10.1007/978-981-19-8069-5_2

Cite this

Snasel, V., Dang, T. K., Pham, P. N. H., Küng, J., & Kong, L. (2022). In-Memory Computing Architectures for Big Data and Machine Learning Applications. In T. K. Dang, J. Küng, & T. M. Chung (Eds.), Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. 9th International Conference, FDSE 2022, Ho Chi Minh City, Vietnam, November 23–25, 2022, Proceedings (Vol. 1688, pp. 19-33). (Communications in Computer and Information Science; Vol. 1688 CCIS). Springer. https://doi.org/10.1007/978-981-19-8069-5_2

Snasel, Vaclav ; Dang, Tran Khanh ; Pham, Phuong N. H. et al. / In-Memory Computing Architectures for Big Data and Machine Learning Applications. Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. 9th International Conference, FDSE 2022, Ho Chi Minh City, Vietnam, November 23–25, 2022, Proceedings. editor / Tran Khanh Dang ; Josef Küng ; Tai M. Chung. Vol. 1688 Springer, 2022. pp. 19-33 (Communications in Computer and Information Science).

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title = "In-Memory Computing Architectures for Big Data and Machine Learning Applications",

abstract = "Traditional computing hardware is working to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial Intelligence algorithms such as Deep Neural Networks and Big Data. In order to get hardware solutions to meet the low-latency and high-throughput computational needs of these algorithms, Non-Von Neumann computing architectures such as In-memory Computing (IMC) have been extensively researched and experimented with over the last five years. This study analyses and reviews works designed to accelerate Machine Learning task. We investigate different architectural aspects and directions and provide our comparative evaluations. We further discuss IMC research{\textquoteright}s challenges and limitations and present possible directions.",

author = "Vaclav Snasel and Dang, \{Tran Khanh\} and Pham, \{Phuong N. H.\} and Josef K{\"u}ng and Lingping Kong",

year = "2022",

doi = "10.1007/978-981-19-8069-5\_2",

language = "English",

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Snasel, V, Dang, TK, Pham, PNH, Küng, J & Kong, L 2022, In-Memory Computing Architectures for Big Data and Machine Learning Applications. in TK Dang, J Küng & TM Chung (eds), Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. 9th International Conference, FDSE 2022, Ho Chi Minh City, Vietnam, November 23–25, 2022, Proceedings. vol. 1688, Communications in Computer and Information Science, vol. 1688 CCIS, Springer, pp. 19-33. https://doi.org/10.1007/978-981-19-8069-5_2

In-Memory Computing Architectures for Big Data and Machine Learning Applications. / Snasel, Vaclav; Dang, Tran Khanh; Pham, Phuong N. H. et al.
Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. 9th International Conference, FDSE 2022, Ho Chi Minh City, Vietnam, November 23–25, 2022, Proceedings. ed. / Tran Khanh Dang; Josef Küng; Tai M. Chung. Vol. 1688 Springer, 2022. p. 19-33 (Communications in Computer and Information Science; Vol. 1688 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

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AU - Kong, Lingping

PY - 2022

Y1 - 2022

N2 - Traditional computing hardware is working to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial Intelligence algorithms such as Deep Neural Networks and Big Data. In order to get hardware solutions to meet the low-latency and high-throughput computational needs of these algorithms, Non-Von Neumann computing architectures such as In-memory Computing (IMC) have been extensively researched and experimented with over the last five years. This study analyses and reviews works designed to accelerate Machine Learning task. We investigate different architectural aspects and directions and provide our comparative evaluations. We further discuss IMC research’s challenges and limitations and present possible directions.

AB - Traditional computing hardware is working to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial Intelligence algorithms such as Deep Neural Networks and Big Data. In order to get hardware solutions to meet the low-latency and high-throughput computational needs of these algorithms, Non-Von Neumann computing architectures such as In-memory Computing (IMC) have been extensively researched and experimented with over the last five years. This study analyses and reviews works designed to accelerate Machine Learning task. We investigate different architectural aspects and directions and provide our comparative evaluations. We further discuss IMC research’s challenges and limitations and present possible directions.

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Snasel V, Dang TK, Pham PNH, Küng J, Kong L. In-Memory Computing Architectures for Big Data and Machine Learning Applications. In Dang TK, Küng J, Chung TM, editors, Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. 9th International Conference, FDSE 2022, Ho Chi Minh City, Vietnam, November 23–25, 2022, Proceedings. Vol. 1688. Springer. 2022. p. 19-33. (Communications in Computer and Information Science). doi: 10.1007/978-981-19-8069-5_2

In-Memory Computing Architectures for Big Data and Machine Learning Applications

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