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

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Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2022)

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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.

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Acknowledgements

The authors gratefully acknowledge financial support DST/INT/Czech/P-12/2019, reg. no. LTAIN19176.

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Václav Snášel & Lingping Kong

  2. Faculty of Information Technology, Ho Chi Minh City University of Food Industry (HUFI), Ho Chi Minh City, Vietnam

    Tran Khanh Dang & Phuong N. H. Pham

  3. Johannes Kepler University Linz, Linz, Austria

    Josef Küng

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  1. Václav Snášel
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  2. Tran Khanh Dang
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  3. Phuong N. H. Pham
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Correspondence to Václav Snášel or Lingping Kong .

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  1. Ho Chi Minh City University of Food Industry, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Seoul, Korea (Republic of)

    Tai M. Chung

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Snášel, 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: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_2

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