Intelligence at the Extreme Edge: A Survey on Reformable TinyML

Rajapakse, Visal, Karunanayake, Ishan and Ahmed, Nadeem 2023. Intelligence at the Extreme Edge: A Survey on Reformable TinyML. ACM Computing Surveys. 55 (13s) 282. https://doi.org/10.1145/3583683

TitleIntelligence at the Extreme Edge: A Survey on Reformable TinyML
TypeJournal article
AuthorsRajapakse, Visal, Karunanayake, Ishan and Ahmed, Nadeem
AbstractTiny Machine Learning (TinyML) is an upsurging research field that proposes to democratize the use of Machine Learning and Deep Learning on highly energy-efficient frugal Microcontroller Units. Considering the general assumption that TinyML can only run inference, growing interest in the domain has led to work that makes them reformable, i.e., solutions that permit models to improve once deployed. This work presents a survey on reformable TinyML solutions with the proposal of a novel taxonomy. Here, the suitability of each hierarchical layer for reformability is discussed. Furthermore, we explore the workflow of TinyML and analyze the identified deployment schemes, available tools and the scarcely available benchmarking tools. Finally, we discuss how reformable TinyML can impact a few selected industrial areas and discuss the challenges, and future directions, and it’s fusion with next-generation AI.
KeywordsGeneral Computer Science
Theoretical Computer Science
Article number282
JournalACM Computing Surveys
Journal citation55 (13s)
ISSN0360-0300
1557-7341
Year2023
PublisherAssociation for Computing Machinery (ACM)
Digital Object Identifier (DOI)https://doi.org/10.1145/3583683
Publication dates
Published online13 Feb 2023
Published in print13 Jul 2023

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