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Computer Science > Computer Vision and Pattern Recognition

arXiv:2108.13732 (cs)
[Submitted on 31 Aug 2021 (v1), last revised 28 Oct 2022 (this version, v2)]

Title:Deep Learning on Edge TPUs

Authors:Yipeng Sun, Andreas M Kist
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Abstract:Computing at the edge is important in remote settings, however, conventional hardware is not optimized for utilizing deep neural networks. The Google Edge TPU is an emerging hardware accelerator that is cost, power and speed efficient, and is available for prototyping and production purposes. Here, I review the Edge TPU platform, the tasks that have been accomplished using the Edge TPU, and which steps are necessary to deploy a model to the Edge TPU hardware. The Edge TPU is not only capable of tackling common computer vision tasks, but also surpasses other hardware accelerators, especially when the entire model can be deployed to the Edge TPU. Co-embedding the Edge TPU in cameras allows a seamless analysis of primary data. In summary, the Edge TPU is a maturing system that has proven its usability across multiple tasks.
Comments: 10 pages, 4 figures, 3 tables
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2108.13732 [cs.CV]
  (or arXiv:2108.13732v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2108.13732

Submission history

From: Andreas Kist [view email]
[v1] Tue, 31 Aug 2021 10:23:37 UTC (713 KB)
[v2] Fri, 28 Oct 2022 20:52:06 UTC (854 KB)
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