Results 111 to 120 of about 2,597 (210)

Energy-aware dynamic programming scheduler for TinyML workloads on energy-harvesting CubeSat-IoT platforms: a comprehensive system modelling and performance analysis

Discover Electronics
The integration of Tiny Machine Learning (TinyML) algorithms into CubeSatInternet of Things (IoT) platforms presents a transformative opportunity for autonomous space-based sensing and decision-making.
Mfonobong Uko, Choice Amaizu, Sunday Ekpo, Gloria Iyawa, Obinna Amaizu   +4 more
doaj   +1 more source

Edge Impulse: An MLOps Platform for Tiny Machine Learning

, 2023
Edge Impulse is a cloud-based machine learning operations (MLOps) platform for developing embedded and edge ML (TinyML) systems that can be deployed to a wide range of hardware targets.
Baaijens, Mathijs, Banbury, Colby, Beavis, Artie, Elium, Alex, Grande, Alessandro, Hymel, Shawn, Jongboom, Jan, Kelcey, Mat, Majchrzycki, Mateusz, Maslov, Dmitry, Moreau, Louis, Plunkett, Jenny, Reddi, Vijay Janapa, Situnayake, Daniel, Tischler, David, Ward, Carl   +15 more
core  

Robust Edge Machine-Learning For The Real-Time Processing And Prediction of Geomagnetic Anomalies and Geomagnetically Induced Currents (GICs) [PDF]

Geomagnetically Induced Currents (GICs) are currents induced in the Earth\u27s surface conductors and are caused by geomagnetic disturbances or storms (GMDs).
Siddique, Talha
core   +1 more source

Certainty-Based Neural Network Architecture Selection Framework for TinyML Systems

IEEE Access
The development of technologies related to the TinyML concept observed in recent years forces us to consider the trade-off between inference time and recognition quality. Moreover, employing data stream processing methods to analyze large volumes of data
Joanna Komorniczak, Tobiasz Puslecki, Pawel Ksieniewicz, Krzysztof Walkowiak   +3 more
doaj   +1 more source

On-device Online Learning and Semantic Management of TinyML Systems

Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning. While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges.
Anicic, Darko, Li, Xue, Ren, Haoyu, Runkler, Thomas A.   +3 more
core   +1 more source

TinyAirNet: TinyML Model Transmission for Energy-efficient Image Retrieval from IoT Devices

This letter introduces an energy-efficient pull-based data collection framework for Internet of Things (IoT) devices that use Tiny Machine Learning (TinyML) to interpret data queries. A TinyML model is transmitted from the edge server to the IoT devices.
Pandey, Shashi Raj, Popovski, Petar, Shiraishi, Junya, Thorsager, Mathias   +3 more
core  

tinyml-rtos-kws

TinyML keyword spotting demo running under FreeRTOS, showing real-time ML inference on Cortex-M devices.
openaire   +1 more source

TinyMetaFed: Efficient Federated Meta-learning for TinyML

Accepted by the ECML PKDD 2023 workshop track: Simplification, Compression, Efficiency, and Frugality for Artificial Intelligence (SCEFA)
Haoyu, Ren, Xue, Li, Darko, Anicic, Thomas A., Runkler   +3 more
openaire   +4 more sources

Lightweight Real-Time Navigation for Autonomous Driving Using TinyML and Few-Shot Learning. [PDF]

Sensors (Basel)
Ali W, Iqbal A, Wadood A, Park H, Kang BO.   +4 more
europepmc   +1 more source

Edge enhanced network monitoring using TinyML [PDF]

TinyML (Tiny Machine Learning) is a rapidly evolving field with applications in wireless communication and edge intelligence. This thesis investigates integrating machine learning models into edge devices to monitor and predict real-time network usage ...
Khan, Ijlal
core