Tiny Machine Learning: Progress and Futures [Feature]
Abstract: 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 …
TinyML: Current Progress, Research Challenges, and Future Roadmap
TinyML: tiny in size, BIG in impact!This paper highlights the current progress, challenges and open research opportunities in the domain of tinyML, benchmarking, and emerging applications for Edge-AI.
A Machine Learning-Oriented Survey on Tiny Machine Learning
Given its multidisciplinary nature, the field of TinyML has been approached from many different angles: this comprehensive survey wishes to provide an up-to-date overview focused on all the learning …
TinyML Applications, Research Challenges, and Future Research ...
The TinyML paradigm advocates the integration of ML-based processes into small devices powered by microcontroller units (MCUs). This article begins with an introduction to TinyML and then explains the …
TinyML: A Systematic Review and Synthesis of Existing Research
Tiny Machine Learning (TinyML), a rapidly evolving edge computing concept that links embedded systems (hardware and software) and machine learning, with the pur
Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML ...
Abstract: Machine Learning (ML) on the edge is key to enabling a new breed of IoT and autonomous system applications. The departure from the traditional cloud-centric architecture means that new …
A Comprehensive Survey on TinyML - IEEE Xplore
Finally, this survey highlights the remaining challenges and points out possible future research directions. We anticipate that this survey will motivate further discussions on the various fields of …
A Smart Gas Sensor Using Machine Learning Algorithms: Sensor Types ...
Apr 24, 2023 · Over the past decade, machine learning (ML) and artificial intelligence (AI) have attracted great interest in research and various practical applications. Curre
Real-Time Road Damage Detection and Infrastructure Evaluation ...
Abstract: Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads.
Tiny Federated Learning for Constrained Sensors: A Systematic ...
In addition to providing a current overview of existing research, this SLR contributes to the advancement and progress of the TinyFL paradigm by providing insights into its challenges, strategies, and future …