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Evaluation of Deep Learning Accelerators for Object Detection at the Edge

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KI 2020: Advances in Artificial Intelligence (KI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12325))

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Abstract

Deep learning is moving more and more from the cloud towards the edge. Therefore, embedded devices are needed that are reasonably cheap, energy-efficient and fast enough. In this paper we evaluate the performance and energy consumption of popular, off-the-shelf commercial devices for deep learning inferencing. We compare the Intel Neural Compute Stick 2, the Google Coral Edge TPU and the Nvidia Jetson Nano with the Raspberry Pi 4 for their suitability as a central controller in an autonomous vehicle for the formula student driverless.

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Notes

  1. 1.

    https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md.

  2. 2.

    https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow.html.

  3. 3.

    https://ngc.nvidia.com/catalog/models/nvidia:ssdtf_fp16.

  4. 4.

    Previous measurements have shown that the type of background and the number of detected objects has no measurable influence on the operating time and energy consumption. We’ve published our dataset on https://github.com/iisys-hof/formula-student-dataset.

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Authors and Affiliations

  1. iisys, Hof University, Alfons-Goppel-Platz1, 95028, Hof, Germany

    Pascal Puchtler & René Peinl

Authors
  1. Pascal Puchtler
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  2. René Peinl
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Correspondence to René Peinl .

Editor information

Editors and Affiliations

  1. Universität Bamberg, Bamberg, Germany

    Ute Schmid

  2. Örebro University, Örebro, Sweden

    Franziska Klügl

  3. Universität Bamberg, Bamberg, Germany

    Diedrich Wolter

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Puchtler, P., Peinl, R. (2020). Evaluation of Deep Learning Accelerators for Object Detection at the Edge. In: Schmid, U., Klügl, F., Wolter, D. (eds) KI 2020: Advances in Artificial Intelligence. KI 2020. Lecture Notes in Computer Science(), vol 12325. Springer, Cham. https://doi.org/10.1007/978-3-030-58285-2_29

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  • DOI: https://doi.org/10.1007/978-3-030-58285-2_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58284-5

  • Online ISBN: 978-3-030-58285-2

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