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A Proposal for FPGA-Accelerated Deep Learning Ensembles in MPSoC Platforms Applied to Malware Detection

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Quality of Information and Communications Technology (QUATIC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1621))

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Abstract

Ensembles of Deep Neural Networks can be profitably employed to improve the overall network performance in a range of applications, including for example online malware detection performed by edge computing systems. In such edge applications, which are often dominated by inference operations, FPGA-based MPSoC platforms may play a competitive role compared to GPU devices because of higher energy efficiency. Furthermore, their hardware reconfiguration capabilities offer a perfect match with the requirement of model diversity posed by Ensemble Learning. This exploratory short paper presents a research plan towards an FPGA-based MPSoC platform exploiting dynamic partial reconfiguration in edge systems for accelerating Deep Learning Ensembles. We present the background and the main rationale behind our envisioned architecture. We also present a preliminary security analysis discussing possible threats and vulnerabilities along with the mitigations enabled by the architecture we plan to develop.

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Notes

  1. 1.

    https://www.xilinx.com/products/design-tools/vitis/vitis-ai.html.

  2. 2.

    https://www.kernel.org/doc/html/v4.19/driver-api/fpga/fpga-mgr.html.

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Acknowledgements

This work was partly founded by the PON “Ricerca e Innovazione” 2014–2020, Azione IV.5, Ministerial Decree n. 1061 of the Italian Ministry of University and Research.

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Authors and Affiliations

  1. Department of Electrical Engineering and Information Technologies (DIETI), University of Naples Federico II, Naples, Italy

    Alessandro Cilardo, Vincenzo Maisto, Nicola Mazzocca & Franca Rocco di Torrepadula

Authors
  1. Alessandro Cilardo
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  2. Vincenzo Maisto
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  3. Nicola Mazzocca
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  4. Franca Rocco di Torrepadula
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Corresponding author

Correspondence to Vincenzo Maisto .

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Editors and Affiliations

  1. University of Malaga, Málaga, Spain

    Antonio Vallecillo

  2. Leiden University, Leiden, The Netherlands

    Joost Visser

  3. University of Castila-La Mancha, Ciudad Real, Spain

    Ricardo Pérez-Castillo

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Cilardo, A., Maisto, V., Mazzocca, N., Rocco di Torrepadula, F. (2022). A Proposal for FPGA-Accelerated Deep Learning Ensembles in MPSoC Platforms Applied to Malware Detection. In: Vallecillo, A., Visser, J., Pérez-Castillo, R. (eds) Quality of Information and Communications Technology. QUATIC 2022. Communications in Computer and Information Science, vol 1621. Springer, Cham. https://doi.org/10.1007/978-3-031-14179-9_16

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  • DOI: https://doi.org/10.1007/978-3-031-14179-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14178-2

  • Online ISBN: 978-3-031-14179-9

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