Sammanfattning
This work investigates the feasibility of using the computational resources of a mobile device in active learning usage scenarios. It addresses the two main concerns, namely a way of fast model training or updating when more labels become available without re-training the whole Deep Learning model in case of image analysis, and the feasibility of running active learning workloads directly on a mobile device to improve the responsiveness and avoid using Cloud computing resources that become expensive at a large scale.
The results found that a mobile phone (Apple iPhone Xs in particular) is superior to CPU-bound workloads on a modern laptop. Two special discoveries relate to the latency of the first prection that turns out to be 20x faster on a phone, and some kind of short-lived acceleration after a user touches a phone’s screen that let small batches of up to 20 images to be processed twice faster than usual, in only 0.1 s for a classification of 20 images.
The results found that a mobile phone (Apple iPhone Xs in particular) is superior to CPU-bound workloads on a modern laptop. Two special discoveries relate to the latency of the first prection that turns out to be 20x faster on a phone, and some kind of short-lived acceleration after a user touches a phone’s screen that let small batches of up to 20 images to be processed twice faster than usual, in only 0.1 s for a classification of 20 images.
Originalspråk | Engelska |
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Titel på värdpublikation | Proceedings of ELM2019 |
Redaktörer | Jiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse |
Antal sidor | 7 |
Utgivningsort | Cham |
Förlag | Springer |
Utgivningsdatum | 2021 |
Sidor | 134-140 |
ISBN (tryckt) | 978-3-030-58988-2 |
ISBN (elektroniskt) | 978-3-030-58989-9 |
DOI | |
Status | Publicerad - 2021 |
MoE-publikationstyp | A4 Artikel i en konferenspublikation |
Publikationsserier
Namn | Proceedings in Adaptation, Learning and Optimization |
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Volym | 14 |
ISSN (tryckt) | 2363-6084 |
ISSN (elektroniskt) | 2363-6092 |
Nyckelord
- 512 Företagsekonomi
- 113 Data- och informationsvetenskap