Results 11 to 20 of about 7,108,814 (163)

Tiny Machine Learning (TinyML): Research trends and future application opportunities

Array
Tiny Machine Learning (TinyML) enables artificial intelligence on low-power edge devices, yet a quantitative understanding of TinyML research remains limited.
Hui Han, Silvana Trimi, Sang M. Lee
doaj   +5 more sources

TinyML with Meta-Learning on Microcontrollers for Air Pollution Prediction

Proceedings
Tiny machine learning (tinyML) involves the application of ML algorithms on resource-constrained devices such as microcontrollers. It is possible to improve tinyML performance by using a meta-learning approach.
I Nyoman Kusuma Wardana, Suhaib A. Fahmy, Julian W. Gardner   +2 more
doaj   +2 more sources

TinyML-Based In-Pipe Feature Detection for Miniature Robots [PDF]

Sensors
Miniature robots in small-diameter pipelines require efficient and reliable environmental perception for autonomous navigation. In this paper, a tiny machine learning (TinyML)-based resource-efficient pipe feature recognition method is proposed for ...
Manman Yang, Andrew Blight, Hitesh Bhardwaj, Nabil Shaukat, Linyan Han, Robert Richardson, Andrew Pickering, George Jackson-Mills, Andrew Barber   +8 more
doaj   +2 more sources

A review on TinyML: State-of-the-art and prospects

Journal of King Saud University: Computer and Information Sciences, 2022
Machine learning has become an indispensable part of the existing technological domain. Edge computing and Internet of Things (IoT) together presents a new opportunity to imply machine learning techniques at the resource constrained embedded devices at ...
Partha Pratim Ray
doaj   +1 more source

Perpetual edge intelligence: adaptive hybrid energy harvesting and reinforcement-learning-based TinyML for autonomous IoT sensors

Discover Electronics
The rapid advancement of Tiny Machine Learning (TinyML) is enabling intelligent inference on highly resource- and energy-constrained Internet of Things (IoT) devices. However, sustaining computation over long operational lifetimes remains a major barrier
Mfonobong Uko, Benjamin Bako, Sunday Ekpo, Gloria Iyawa   +3 more
doaj   +2 more sources

Widening Access to Applied Machine Learning With TinyML [PDF]

, 2022
Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our
Krishnan, Srivatsan, Reddi, Vijay, Warden, Pete, Lam, Maximilian, Tingley, Dustin, Plancher, Brian, Banzi, Massimo, Bennett, Matthew, Ghosal, Radhika, Smith, J. Evan, Ramaprasad, Dhilan, Chitlangia, Sharad, Pajak, Dominic, Banbury, Colby, Leiker, Daniel, Moroney, Laurence, Grafman, Sarah, Jaeger, Rupert, Agarwal, Anant, Kennedy, Susan, Mazumder, Mark, Stewart, Matthew, Brown, Benjamin, Suzuki, Lara, Mann, Cara   +24 more
core   +3 more sources

CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (TinyML) Acceleration on FPGAs

2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2023
Need for the efficient processing of neural networks has given rise to the development of hardware accelerators. The increased adoption of specialized hardware has highlighted the need for more agile design flows for hardware-software co-design and domain-specific optimizations.
Shvetank Prakash, Tim Callahan, Joseph Bushagour, Colby R. Banbury, Alan V. Green, Pete Warden, Tim Ansell, Vijay Janapa Reddi   +7 more
openaire   +4 more sources

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

Micromachines, 2022
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.
Norah N. Alajlan, Dina M. Ibrahim
doaj   +1 more source

A Machine Learning-oriented Survey on Tiny Machine Learning [PDF]

, 2023
The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures.
Capogrosso, Luigi, Cunico, Federico, Cheng, Dong Seon, Fummi, Franco, Cristani, Marco   +4 more
core   +1 more source

An open‐source tool for evaluating calibration techniques used in low‐cost air pollutant monitors

Electronics Letters, Volume 59, Issue 10, May 2023., 2023
In this paper the structure of an open‐source tool is introduced for evaluation of calibration techniques used in low‐cost air pollutant monitors. Different algorithms can be configured and used, such as regression techniques and machine learning classifiers. In addition, this tool was implemented to be compatible with any input dataset. These features
Daniel Trevisan Tatsch, Alejandro Rafael Garcia Ramirez, Fernando Campo, Leonardo Hoinaski, Evelio González‐Dalmau   +4 more
wiley   +1 more source