mlsanityData to 14 Oct 2026

Deep Learning on Edge TPUs

arXiv 2108.13732 ↗First posted 31 Aug 2021cs.CV cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 20 of its 45 references that are in the map. First posted on arXiv 31 Aug 2021.

Builds on

20 of 45 references.

References · 20

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8 more
  1. 2020Accelerator-aware Neural Network Design using AutoMLcited by 30
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Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 1.

3 papers.

  1. 2025From Tiny Machine Learning to Tiny Deep Learning: A Surveycited by 5
  2. 2023Saturn: An Optimized Data System for Large Model Deep Learning Workloadscited by 2
  3. 2023Neural Network Methods for Radiation Detectors and Imagingcited by 2