Results 11 to 20 of about 16,826 (269)
Tiny Machine Learning for Concept Drift [PDF]
IEEE Transactions on Neural Networks and Learning SystemsTiny Machine Learning (TML) is a new research area whose goal is to design machine and deep learning techniques able to operate in Embedded Systems and IoT units, hence satisfying the severe technological constraints on memory, computation, and energy characterizing these pervasive devices.
Simone Disabato, Manuel Roveri
exaly +7 more sources
Tiny Machine Learning Battery State-of-Charge Estimation Hardware Accelerated [PDF]
Applied SciencesElectric mobility is pervasive and strongly affects everyone in everyday life. Motorbikes, bikes, cars, humanoid robots, etc., feature specific battery architectures composed of several lithium nickel oxide cells. Some of them are connected in series and
Danilo Pietro Pau, Alberto Aniballi
doaj +4 more sources
Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots [PDF]
2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2022 4 pages, 3 figures, 1 table, in IEEE AICAS ...
Sabrina M. Neuman +10 more
exaly +5 more sources
Navigating the Challenges and Opportunities of Tiny Deep Learning and Tiny Machine Learning in Lung Cancer Identification [PDF]
مجلة النهرين للعلوم الهندسيةLung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens.
Yasir Salam Abdulghafoor +2 more
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Tiny Machine Learning: Progress and Futures [PDF]
CoRR, 2023 arXiv admin note: text overlap with arXiv:2206 ...
Ji Lin 0002 +4 more
core +4 more sources
Tiny Machine Learning for Resource-Constrained Microcontrollers [PDF]
Journal of Sensors, 2022 We use 250 billion microcontrollers daily in electronic devices that are capable of running machine learning models inside them. Unfortunately, most of these microcontrollers are highly constrained in terms of computational resources, such as memory usage or clock speed.
Hämäläinen, Timo, Immonen, Riku
core +7 more sources
Deep and Wide Tiny Machine Learning [PDF]
, 2022 AbstractIn the last decades, on the one hand, Deep Learning (DL) has become state of the art in several domains, e.g., image classification, object detection, and natural language processing. On the other hand, pervasive technologies—Internet of Things (IoT) units, embedded systems, and Micro-Controller Units (MCUs)—ask for intelligent processing ...
Disabato, Simone
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Tiny Machine Learning (TinyML): Research trends and future application opportunities [PDF]
ArrayTiny 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
Future InternetEmerging edge devices are transforming the Internet of Things (IoT) by enabling more responsive and efficient interactions between physical objects and digital networks.
Vlad-Eusebiu Baciu +3 more
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Physics-Aware Tiny Machine Learning [PDF]
, 2023 Tiny machine learning has enabled Internet of Things platforms to make intelligent inferences for time-critical and remote applications from unstructured data. However, realizing edge artificial intelligence systems that can perform long-term high-level reasoning and obey the underlying system physics, rules, and constraints within the tight platform ...
Saha, Swapnil Sayan
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