A review of TinyML
In this current technological world, the application of machine learning is becoming ubiquitous. Incorporating machine learning algorithms on extremely low-power and inexpensive embedded devices at the edge level is now possible due to the combination of the Internet of Things (IoT) and edge computing. To estimate an outcome, traditional machine learning demands vast amounts of resources. The TinyML concept for embedded machine learning attempts to push such diversity from usual high-end approaches to low-end applications. TinyML is a rapidly expanding interdisciplinary topic at the convergence of machine learning, software, and hardware centered on deploying deep neural network models on embedded (micro-controller-driven) systems. TinyML will pave the way for novel edge-level services and applications that survive on distributed edge inferring and independent decision-making rather than server computation. In this paper, we explore TinyML's methodology, how TinyML can benefit a few specific industrial fields, its obstacles, and its future scope.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingDiversityEdge-computingSimilar Papers 제목 키워드 기반
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review
The field of Tiny Machine Learning (TinyML) has gained significant attention due to its potential to enable intelligent applications on resource-constrained devices. This review provides an in-depth analysis of the advan…
Model CompressionQuantizationSoftware Engineering Approaches for TinyML based IoT Embedded Vision: A Systematic Literature Review
Internet of Things (IoT) has catapulted human ability to control our environments through ubiquitous sensing, communication, computation, and actuation. Over the past few years, IoT has joined forces with Machine Learnin…
BIG-bench Machine LearningSystematic Literature ReviewTinyML Towards Industry 4.0: Resource-Efficient Process Monitoring of a Milling Machine
In the context of industry 4.0, long-serving industrial machines can be retrofitted with process monitoring capabilities for future use in a smart factory. One possible approach is the deployment of wireless monitoring s…
Tiny Machine Learning: Progress and Futures
Tiny Machine Learning (TinyML) is a new frontier of machine learning. By squeezing deep learning models into billions of IoT devices and microcontrollers (MCUs), we expand the scope of AI applications and enable ubiquito…
Deep LearningTinyML: Tools, Applications, Challenges, and Future Research Directions
In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably, conventional ML techniques require enormous amounts of power to meet the…
Edge-computing