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On the edge — deploying deep learning applications on mobile

Techniques on striking the efficiency-accuracy trade-off for deep neural networks on constrained devices

Aliaksei Mikhailiuk
Towards Data Science
9 min readJul 31, 2022

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Image by the Author.

So many AI advancements get to headlines: “AI is beating humans in Go!”; “Deep weather forecasting”; “Talking Mona Lisa painting”… And yet I do not feel too excited… Despite the appeal on the outlook, these results are achieved with models that are sound proof of concept but are still too far from the real world applications. And the reason for that is simple — their size.

Bigger models with bigger datasets get better results. But these are neither sustainable in terms of the physical resources they consume, such as memory and power, nor in inference times, which are very far from the real-time performance required for many applications.

Real-life problems require smaller models that can run on constrained devices. And with broader security and privacy concerns, there are more and more pros for having models that can fit on a device, eliminating any data transfer to the servers.

Below I go over techniques that make models feasible for constrained devices, such as mobile phones. To make that possible, we reduce the model’s spatial complexity and inference time and organize data flow such that…

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Towards Data Science
Towards Data Science

Published in Towards Data Science

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Aliaksei Mikhailiuk
Aliaksei Mikhailiuk

Written by Aliaksei Mikhailiuk

Tech Lead Manager at Snap. Ex-AI Team Lead at Huawei, PhD from University of Cambridge https://www.linkedin.com/in/aliakseimikhailiuk/

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