High-Performance ELM for Memory Constrained Edge Computing Devices with Metal Performance Shaders

Anton Akusok; Leonardo Espinosa-Leal; Kaj-Mikael Björk; Amaury Lendasse

doi:10.1007/978-3-030-58989-9_9

High-Performance ELM for Memory Constrained Edge Computing Devices with Metal Performance Shaders

Anton Akusok^*, Leonardo Espinosa-Leal, Kaj-Mikael Björk, Amaury Lendasse

^*Corresponding author for this work

Not published at VTT

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

This paper proposes a block solution method for the Extreme Learning Machine. It combines the speed of a direct non-iterative solver with minimal memory requirements. The method is suitable for edge computing scenarios running on a mobile device with GPU acceleration. The implementation tested on the GPU of iPad Pro outperforms a laptop CPU, and trains a 19,000-neuron model using under one gigabyte of memory. It confirms the feasibility of Big Data analysis on modern mobile devices.

Original language	English
Title of host publication	Proceedings of ELM2019
Editors	Jiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse
Place of Publication	Cham
Publisher	Springer
Pages	79-88
ISBN (Electronic)	978-3-030-58989-9
ISBN (Print)	978-3-030-58988-2, 978-3-030-59049-9
DOIs	https://doi.org/10.1007/978-3-030-58989-9_9
Publication status	Published - 12 Sept 2020
MoE publication type	A4 Article in a conference publication
Event	2019 International Conference on Extreme Learning Machine (ELM 2019) - Yangzhou, China Duration: 14 Dec 2019 → 16 Dec 2019

Publication series

Series	Proceedings in Adaptation, Learning and Optimization
Volume	14
ISSN	2363-6084

Conference

Conference	2019 International Conference on Extreme Learning Machine (ELM 2019)
Country/Territory	China
City	Yangzhou
Period	14/12/19 → 16/12/19

Access to Document

10.1007/978-3-030-58989-9_9

Cite this

@inproceedings{7a0b9e5a5edf42aea056daf0e55544ea,

title = "High-Performance ELM for Memory Constrained Edge Computing Devices with Metal Performance Shaders",

abstract = "This paper proposes a block solution method for the Extreme Learning Machine. It combines the speed of a direct non-iterative solver with minimal memory requirements. The method is suitable for edge computing scenarios running on a mobile device with GPU acceleration. The implementation tested on the GPU of iPad Pro outperforms a laptop CPU, and trains a 19,000-neuron model using under one gigabyte of memory. It confirms the feasibility of Big Data analysis on modern mobile devices.",

author = "Anton Akusok and Leonardo Espinosa-Leal and Kaj-Mikael Bj{\"o}rk and Amaury Lendasse",

year = "2020",

month = sep,

day = "12",

doi = "10.1007/978-3-030-58989-9\_9",

language = "English",

isbn = "978-3-030-58988-2",

series = "Proceedings in Adaptation, Learning and Optimization",

publisher = "Springer",

pages = "79--88",

editor = "Jiuwen Cao and Vong, \{Chi Man\} and Yoan Miche and Amaury Lendasse",

booktitle = "Proceedings of ELM2019",

address = "Germany",

note = "2019 International Conference on Extreme Learning Machine (ELM 2019) ; Conference date: 14-12-2019 Through 16-12-2019",

}

Akusok, A, Espinosa-Leal, L, Björk, K-M & Lendasse, A 2020, High-Performance ELM for Memory Constrained Edge Computing Devices with Metal Performance Shaders. in J Cao, CM Vong, Y Miche & A Lendasse (eds), Proceedings of ELM2019. Springer, Cham, Proceedings in Adaptation, Learning and Optimization, vol. 14, pp. 79-88, 2019 International Conference on Extreme Learning Machine (ELM 2019), Yangzhou, China, 14/12/19. https://doi.org/10.1007/978-3-030-58989-9_9

High-Performance ELM for Memory Constrained Edge Computing Devices with Metal Performance Shaders. / Akusok, Anton; Espinosa-Leal, Leonardo; Björk, Kaj-Mikael et al.
Proceedings of ELM2019. ed. / Jiuwen Cao; Chi Man Vong; Yoan Miche; Amaury Lendasse. Cham: Springer, 2020. p. 79-88 (Proceedings in Adaptation, Learning and Optimization, Vol. 14).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - High-Performance ELM for Memory Constrained Edge Computing Devices with Metal Performance Shaders

AU - Akusok, Anton

AU - Espinosa-Leal, Leonardo

AU - Björk, Kaj-Mikael

AU - Lendasse, Amaury

PY - 2020/9/12

Y1 - 2020/9/12

N2 - This paper proposes a block solution method for the Extreme Learning Machine. It combines the speed of a direct non-iterative solver with minimal memory requirements. The method is suitable for edge computing scenarios running on a mobile device with GPU acceleration. The implementation tested on the GPU of iPad Pro outperforms a laptop CPU, and trains a 19,000-neuron model using under one gigabyte of memory. It confirms the feasibility of Big Data analysis on modern mobile devices.

AB - This paper proposes a block solution method for the Extreme Learning Machine. It combines the speed of a direct non-iterative solver with minimal memory requirements. The method is suitable for edge computing scenarios running on a mobile device with GPU acceleration. The implementation tested on the GPU of iPad Pro outperforms a laptop CPU, and trains a 19,000-neuron model using under one gigabyte of memory. It confirms the feasibility of Big Data analysis on modern mobile devices.

U2 - 10.1007/978-3-030-58989-9_9

DO - 10.1007/978-3-030-58989-9_9

M3 - Conference article in proceedings

SN - 978-3-030-58988-2

SN - 978-3-030-59049-9

T3 - Proceedings in Adaptation, Learning and Optimization

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BT - Proceedings of ELM2019

A2 - Cao, Jiuwen

A2 - Vong, Chi Man

A2 - Miche, Yoan

A2 - Lendasse, Amaury

PB - Springer

CY - Cham

T2 - 2019 International Conference on Extreme Learning Machine (ELM 2019)

Y2 - 14 December 2019 through 16 December 2019

ER -

High-Performance ELM for Memory Constrained Edge Computing Devices with Metal Performance Shaders

Abstract

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