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
| Title | Intelligence at the Extreme Edge: A Survey on Reformable TinyML |
|---|---|
| Type | Journal article |
| Authors | Rajapakse, Visal, Karunanayake, Ishan and Ahmed, Nadeem |
| Abstract | Tiny 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. |
| Keywords | General Computer Science |
| Theoretical Computer Science | |
| Article number | 282 |
| Journal | ACM Computing Surveys |
| Journal citation | 55 (13s) |
| ISSN | 0360-0300 |
| 1557-7341 | |
| Year | 2023 |
| Publisher | Association for Computing Machinery (ACM) |
| Digital Object Identifier (DOI) | https://doi.org/10.1145/3583683 |
| Publication dates | |
| Published online | 13 Feb 2023 |
| Published in print | 13 Jul 2023 |