Abstrakt

In recent years, companies, such as Intel and Google, have brought onto the market small low-power platforms that can be used to deploy and run inference of Deep Neural Networks at a low cost. These platforms can process data at the edge, such as images from a camera, to avoid transfer of large amount of data across a network. To determine which platform to use for a specific task, practitioners usually compare parameters, such as inference time and power consumption. However, to provide a better incentive on platform selection based on requirements, it is important to also consider the platform price.
In this paper, we explore platform/model trade-offs, by providing benchmarks of state-of-the-art platforms within three common computer vision tasks; classification, detection and segmentation. By also considering the price of each platform, we provide a comparison of price versus inference time, to aid quick decision making in regard to platform and model selection. Finally, by analysing the operation allocation of models for each platform, we identify operations that should be optimised, based on platform/model selection.
OriginalsprogEngelsk
TitelSpringer LNCS series
ForlagSpringer
StatusAccepteret/In press - 11 jan. 2021
Begivenhed1st International Workshop on Industrial Machine Learning at 25th International Conference on Pattern Recognition -
Varighed: 11 jan. 2021 → …
https://sites.google.com/view/iml2020

Konference

Konference1st International Workshop on Industrial Machine Learning at 25th International Conference on Pattern Recognition
Periode11/01/2021 → …
Internetadresse

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