Zaidi, SAR orcid.org/0000-0003-1969-3727, Hayajneh, AM, Hafeez, M et al. (1 more author) (2022) Unlocking Edge Intelligence through Tiny Machine Learning (TinyML). IEEE Access, 10. ISSN 2169-3536
Abstract
Machine Learning (ML) on the edge is key for enabling a new breed of IoT and autonomous system applications. The departure from the traditional cloud-centric architecture means that new deployments can be more power-efficient, provide better privacy and reduced latency for inference. At the core of this paradigm is TinyML, a framework allowing the execution of ML models on low-power embedded devices. TinyML allows importing pre-trained ML models on the edge for providing ML-as-a-Service (MLaaS) to IoT devices. This article presents a comprehensive overview of Tiny MLaaS (TMLaaS) architecture. The TMLaaS architecture inherently presents several design trade-offs in terms of energy consumption, security, privacy, and latency.We also present how TMLaaS architecture can be implemented, deployed, and maintained for large scale IoT deployment. The feasibility of implementation for the TMLaaS architecture has been demonstrated with the help of a case study.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022, The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. |
Keywords: | Tiny Machine Learning , IoT , Edge Computing , 5G , LoRa , Gesture Recognition , Deep Learning , Transfer Learning , Federated Learning , Implementation , MLOps , Energy Efficiency |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Sep 2022 13:17 |
Last Modified: | 07 Jul 2023 14:50 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ACCESS.2022.3207200 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190986 |
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