Unlocking Edge Intelligence through Tiny Machine Learning (TinyML)

Syed Ali Raza Zaidi, Ali Hayajneh, Maryam Hafeez, Qasim Ahmed

Research output: Contribution to journalArticlepeer-review

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-aService (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
Original languageEnglish
Article number9893787
Number of pages11
JournalIEEE Access
Early online date16 Sep 2022
DOIs
Publication statusE-pub ahead of print - 16 Sep 2022

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