OTA-TinyML: Over the air deployment of TinyML models and execution on IoT devices

Sudharsan, Bharath, Breslin, John G., Tahir, Mehreen, Intizar Ali, Muhammad, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646, Dustdar, Schahram and Ranjan, Rajiv 2022. OTA-TinyML: Over the air deployment of TinyML models and execution on IoT devices. IEEE Internet Computing 26 (3) , pp. 69-78. 10.1109/MIC.2021.3133552

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Abstract

This article presents a novel over-the-air (OTA) technique to remotely deploy tiny ML models over Internet of Things (IoT) devices and perform tasks, such as machine learning (ML) model updates, firmware reflashing, reconfiguration, or repurposing. We discuss relevant challenges for OTA ML deployment over IoT both at the scientific and engineering level. We propose OTA-TinyML to enable resource-constrained IoT devices to perform end-to-end fetching, storage, and execution of many TinyML models. OTA-TinyML loads the C source file of ML models from a web server into the embedded IoT devices via HTTPS. OTA-TinyML is tested by performing remote fetching of six types of ML models, storing them on four types of memory units, then loading and executing on seven popular MCU boards.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1089-7801
Date of First Compliant Deposit:	1 July 2022
Date of Acceptance:	20 June 2022
Last Modified:	06 Nov 2023 22:52
URI:	https://orca.cardiff.ac.uk/id/eprint/150971

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