TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications

被引:68
作者
Alajlan, Norah N. [1 ]
Ibrahim, Dina M. [1 ,2 ]
机构
[1] Qassim Univ, Coll Comp, Dept Informat Technol, Buraydah 51452, Saudi Arabia
[2] Tanta Univ, Fac Engn, Dept Comp & Control Engn, Tanta 31733, Egypt
关键词
Internet of Things; edge devices; machine learning; deep learning; tiny machine learning;
D O I
10.3390/mi13060851
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are placed in various fields. Many of these devices are based on machine learning (ML) models, which render them intelligent and able to make decisions. IoT devices typically have limited resources, which restricts the execution of complex ML models such as deep learning (DL) on them. In addition, connecting IoT devices to the cloud to transfer raw data and perform processing causes delayed system responses, exposes private data and increases communication costs. Therefore, to tackle these issues, there is a new technology called Tiny Machine Learning (TinyML), that has paved the way to meet the challenges of IoT devices. This technology allows processing of the data locally on the device without the need to send it to the cloud. In addition, TinyML permits the inference of ML models, concerning DL models on the device as a Microcontroller that has limited resources. The aim of this paper is to provide an overview of the revolution of TinyML and a review of tinyML studies, wherein the main contribution is to provide an analysis of the type of ML models used in tinyML studies; it also presents the details of datasets and the types and characteristics of the devices with an aim to clarify the state of the art and envision development requirements.
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页数:22
相关论文
共 64 条
[1]  
Akash ASL Alphabet from Kaggle, 2020, Vestiaire Collective Dataset (Version 2) Dataset. Kaggle
[2]  
[Anonymous], 2020, ASHISH JANGRA FACE M
[3]  
Avigan A., 2020, CLEVELAND CLIN HEART
[4]  
Banbury Colby R, 2020, ArXiv
[5]   Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions [J].
Ben Atitallah, Safa ;
Driss, Maha ;
Boulila, Wadii ;
Ben Ghezala, Henda .
COMPUTER SCIENCE REVIEW, 2020, 38
[6]   A review on deep learning for future smart cities [J].
Bhattacharya, Sweta ;
Somayaji, Siva Rama Krishnan ;
Gadekallu, Thippa Reddy ;
Alazab, Mamoun ;
Maddikunta, Praveen Kumar Reddy .
INTERNET TECHNOLOGY LETTERS, 2022, 5 (01)
[7]   Statistical fingerprint-based intrusion detection system (SF-IDS) [J].
Boero, Luca ;
Cello, Marco ;
Marchese, Mario ;
Mariconti, Enrico ;
Naqash, Talha ;
Zappatore, Sandro .
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, 2017, 30 (10)
[8]  
Bulat Adrian, 2019, BMVC
[9]  
Cai H., 2020, arXiv
[10]   CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices [J].
Capotondi, Alessandro ;
Rusci, Manuele ;
Fariselli, Marco ;
Benini, Luca .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2020, 67 (05) :871-875