survey

Intelligence at the Extreme Edge: A Survey on Reformable TinyML

Authors:

Visal Rajapakse,

Ishan Karunanayake,

Nadeem AhmedAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 13s

Article No.: 282, Pages 1 - 30

https://doi.org/10.1145/3583683

Published: 13 July 2023 Publication History

Abstract

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 (MCUs). 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 its fusion with next-generation AI.

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Index Terms

Intelligence at the Extreme Edge: A Survey on Reformable TinyML
1. Computing methodologies
  1. Artificial intelligence
2. General and reference
  1. Document types
    1. Surveys and overviews

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 13s

December 2023

1367 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3606252

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University of Sydney, Australia

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Publication History

Published: 13 July 2023

Online AM: 13 February 2023

Accepted: 30 January 2023

Revised: 06 January 2023

Received: 15 April 2022

Published in CSUR Volume 55, Issue 13s

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