Results 11 to 20 of about 2,210 (159)

A Comprehensive Survey on TinyML

IEEE Access, 2023
Recent spectacular progress in computational technologies has led to an unprecedented boom in the field of Artificial Intelligence (AI). AI is now used in a plethora of research areas and has demonstrated its capability to bring new approaches and ...
Youssef Abadade, Anas Temouden, Hatim Bamoumen, Nabil Benamar, Yousra Chtouki, Abdelhakim Senhaji Hafid   +5 more
doaj   +2 more sources

A Holistic Review of the TinyML Stack for Predictive Maintenance

IEEE Access
Downtime caused by failing equipment can be extremely costly for organizations. Predictive Maintenance (PdM), which uses data to predict when maintenance should be conducted, is an essential tool for increasing safety, maximizing uptime and minimizing ...
Emil Njor, Mohammad Amin Hasanpour, Jan Madsen, Xenofon Fafoutis   +3 more
doaj   +2 more sources

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   +2 more sources

On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case [PDF]

IEEE Access
As technology advances, the use of Machine Learning (ML) in cybersecurity is becoming increasingly crucial to tackle the growing complexity of cyber threats.
Fatemeh Dehrouyeh, Li Yang, Firouz Badrkhani Ajaei, Abdallah Shami   +3 more
doaj   +3 more sources

Benchmarking TinyML Systems: Challenges and Direction [PDF]

, 2020
Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an entirely new class of smart applications. However, continued progress is limited by the lack of a widely accepted benchmark for these systems.
Huang, Xinyuan, Fazel, Amin, Patterson, David, Lokhmotov, Anton, Lam, Max, Kanter, David, Banbury, Colby R., Yadav, Poonam, Seo, Jae-sun, Fu, William, Hurtado, Robert, Pau, Danilo, Holleman, Jeremy, Sieracki, Jeff, Thakker, Urmish, Verhelst, Marian, Reddi, Vijay Janapa   +16 more
core   +6 more sources

Exploring opportunities in TinyML [PDF]

, 2022
Internet of Things (IoT) has acquired useful and powerful advances thanks to the Machine Learning (ML) implementations. But the implementation of Machine Learning in IoT devices with data centers has some serious problems (data privacy, network ...
Rubio Serrano, Juan Diego
core   +2 more sources

A review of TinyML [PDF]

, 2022
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
Yelchuri, Harsha, R, Rashmi
core   +1 more source

TinyML on-device neural network training [PDF]

, 2022
LAUREA MAGISTRALETiny Machine Learning (TinyML) è l'ambito di ricerca in cui si combinano le soluzioni di apprendimento automatico con i vincoli stringenti del hardware embedded/IoT.
Ostrovan, Eugeniu
core  

Earthquake Detection with tinyML

, 2023
Earthquake detection is the critical first step in earthquake early warning (EEW) systems. For robust EEW systems, detection accuracy, detection latency, and sensor density are critical to providing real-time earthquake alerts.
Timothy Clements, Clements, Timothy
core   +1 more source

A Primer for tinyML Predictive Maintenance: Input and Model Optimisation

, 2022
In this paper, we investigate techniques used to optimise tinyML based Predictive Maintenance (PdM). We first describe PdM andtinyML and how they can provide an alternative to cloud-based PdM.
Madsen, Jan, Fafoutis, Xenofon, Njor, Emil, Jørgensen Njor, Emil   +3 more
core   +1 more source