TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review

Nikolaos Schizas, Aristeidis Karras (), Christos Karras and Spyros Sioutas
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Nikolaos Schizas: Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece
Aristeidis Karras: Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece
Christos Karras: Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece
Spyros Sioutas: Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece

Future Internet, 2022, vol. 14, issue 12, 1-45

Abstract: The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within edge devices. In particular, the TinyML framework in such devices aims to deliver reduced latency, efficient bandwidth consumption, improved data security, increased privacy, lower costs and overall network cost reduction in cloud environments. Its ability to enable IoT devices to work effectively without constant connectivity to cloud services, while nevertheless providing accurate ML services, offers a viable alternative for IoT applications seeking cost-effective solutions. TinyML intends to deliver on-premises analytics that bring significant value to IoT services, particularly in environments with limited connection. This review article defines TinyML, presents an overview of its benefits and uses and provides background information based on up-to-date literature. Then, we demonstrate the TensorFlow Lite framework which supports TinyML along with analytical steps for an ML model creation. In addition, we explore the integration of TinyML with network technologies such as 5G and LPWAN. Ultimately, we anticipate that this analysis will serve as an informational pillar for the IoT/Cloud research community and pave the way for future studies.

Keywords: Internet of Things; large scale IoT deployments; TinyML; edge computing; edge AI; edge ML; machine learning; deep learning; Tensorflow Lite; TFLM; LPWAN; 5G (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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