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A Heterogeneous and Reconfigurable Embedded Architecture for Energy-Efficient Execution of Convolutional Neural Networks

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Architecture of Computing Systems – ARCS 2019 (ARCS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11479))

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Abstract

Machine learning based convolutional neural networks (CNN) are becoming increasingly popular for identification tasks like image classification or speech recognition. However, CNNs have high memory and computational demands which makes it challenging to implement them on cost-efficient and energy-autonomous hardware. To cope with this challenge we present a heterogeneous and reconfigurable embedded architecture implemented on an inexpensive and widely available entry-level system on chip (SoC). Our architecture combines an ARM CPU and a coarse-grained reconfigurable architecture (CGRA) which execute a CNN in parallel to reach a higher energy-efficiency. Our results show up to 130% higher performance and 78% better energy-efficiency compared with an embedded Nvidia GPU.

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Acknowledgments

This work has been partially funded by the Stiftung Industrieforschung through the scholarship for master’s theses and the German Federal Ministry of Education and Research (BMBF) under grant number 16ES0876 (GENIAL!).

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Authors and Affiliations

  1. Department of Computer Science, University of Tübingen, Tübingen, Germany

    Konstantin Lübeck & Oliver Bringmann

Authors
  1. Konstantin Lübeck
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  2. Oliver Bringmann
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Corresponding author

Correspondence to Konstantin Lübeck .

Editor information

Editors and Affiliations

  1. Technical University of Denmark, Lyngby, Denmark

    Martin Schoeberl

  2. Technical University of Darmstadt, Darmstadt, Germany

    Christian Hochberger

  3. Airbus Defence and Space GmbH, Taufkirchen, Germany

    Sascha Uhrig

  4. University of Hanover, Hanover, Germany

    Jürgen Brehm

  5. Otto-von-Guericke University, Magdeburg, Germany

    Thilo Pionteck

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Lübeck, K., Bringmann, O. (2019). A Heterogeneous and Reconfigurable Embedded Architecture for Energy-Efficient Execution of Convolutional Neural Networks. In: Schoeberl, M., Hochberger, C., Uhrig, S., Brehm, J., Pionteck, T. (eds) Architecture of Computing Systems – ARCS 2019. ARCS 2019. Lecture Notes in Computer Science(), vol 11479. Springer, Cham. https://doi.org/10.1007/978-3-030-18656-2_20

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  • DOI: https://doi.org/10.1007/978-3-030-18656-2_20

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-18656-2

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