ArticleMicromachines2022
TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications.
Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- AI-driven technological breakthroughs and practical pathways for biodiversity conservation and ecological management.iScience · 2026Review
- Smart Furniture-Embedded Bed-Interaction and Multimodal Room Sensing with Sleep-Aware Hierarchical Fusion for Control-Oriented Bedroom State Recognition.Sensors (Basel, Switzerland) · 2026Article
- Article
- A Cloud-Aware Scalable Architecture for Distributed Edge-Enabled BCI Biosensor System.Biosensors · 2026Article
- IoT framework for sports activity safety monitoring based on wearable sensors and CRNN spatiotemporal analysis.Scientific reports · 2026Article
- Artificial Intelligence-Enhanced Wearable Blood Pressure Monitoring in Resource-Limited Settings: A Co-Design of Sensors, Model, and Deployment.Nano-micro letters · 2026Review
- Muscle myography for human-machine interfaces: a review of sensing modalities and control interfaces.Frontiers in bioengineering and biotechnology · 2026Review
- The Development of a Wearable-Based System for Detecting Shaken Baby Syndrome Using Machine Learning Models.Sensors (Basel, Switzerland) · 2025Article
- TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing.Scientific reports · 2025Article
- A Wireless Sensor Network-Based Combustible Gas Detection System Using PSO-DBO-Optimized BP Neural Network.Sensors (Basel, Switzerland) · 2025Article
- Securing the CAN bus using deep learning for intrusion detection in vehicles.Scientific reports · 2025Article
- Dynamic Performance and Power Optimization with Heterogeneous Processing-in-Memory for AI Applications on Edge Devices.Micromachines · 2024Article
- TinyNS: Platform-Aware Neurosymbolic Auto Tiny Machine Learning.ACM transactions on embedded computing systems : TECS · 2024Article
- Heart Rate Measurement Using the Built-In Triaxial Accelerometer from a Commercial Digital Writing Device.Sensors (Basel, Switzerland) · 2024Article
- Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose.Sensors (Basel, Switzerland) · 2024Article
- Evaluation of a Machine Learning Algorithm to Classify Ultrasonic Transducer Misalignment and Deployment Using TinyML.Sensors (Basel, Switzerland) · 2024Article
- DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Editorial for the Special Issue on Micro and Smart Devices and Systems.Micromachines · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.