Results 81 to 90 of about 374 (124)

TinyML Platforms Benchmarking

Lecture Notes in Electrical Engineering, 2022
Recent advances in state-of-the-art ultra-low power embedded devices for machine learning (ML) have permitted a new class of products whose key features enable ML capabilities on microcontrollers with less than 1 mW power consumption (TinyML). TinyML provides a unique solution by aggregating and analyzing data at the edge on low-power embedded devices.
Luca Gemma, Davide Brunelli, Brunelli Davide   +2 more
exaly   +4 more sources

OTA-TinyML: Over the Air Deployment of TinyML Models and Execution on IoT Devices

IEEE Internet Computing, 2022
This article presents a novel over-the-air (OTA) technique to remotely deploy tiny ML models over Internet of Things (IoT) devices and perform tasks, such as machine learning (ML) model updates, firmware reflashing, reconfiguration, or repurposing. We discuss relevant challenges for OTA ML deployment over IoT both at the scientific and engineering ...
Bharath Sudharsan, John G Breslin, Mehreen Tahir   +2 more
exaly   +2 more sources

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Is TinyML Sustainable?

Communications of the ACM, 2023
Assessing the environmental impacts of machine learning on microcontrollers.
Shvetank Prakash, Matthew Stewart, Colby R. Banbury, Mark Mazumder, Pete Warden, Brian Plancher, Vijay Janapa Reddi   +6 more
openaire   +1 more source

TinyML Meets IoT: A Comprehensive Survey

Internet of Things (Netherlands), 2021
Abstract The rapid growth in miniaturization of low-power embedded devices and advancement in the optimization of machine learning (ML) algorithms have opened up a new prospect of the Internet of Things (IoT), tiny machine learning (TinyML), which calls for implementing the ML algorithm within the IoT device .
Lachit Dutta
exaly   +2 more sources

A Review on the emerging technology of TinyML

ACM Computing Surveys
Tiny Machine Learning (TinyML) is an emerging technology proposed by the scientific community for developing autonomous and secure devices that can gather, process, and provide results without transferring data to external entities. The technology aims to democratize AI by making it available to more sectors and contribute to the digital revolution of ...
Vasileios Tsoukas, Anargyros Gkogkidis, Eleni Boumpa   +2 more
exaly   +2 more sources

Earthquake Detection with tinyML

Seismological Research Letters, 2023
Abstract 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.
openaire   +3 more sources

IP Protection in TinyML

2023 60th ACM/IEEE Design Automation Conference (DAC), 2023
Jinwen Wang, Yuhao Wu 0006, Han Liu, Bo Yuan 0002, Roger D. Chamberlain, Ning Zhang 0017   +5 more
openaire   +1 more source

Special Issue on TinyML

IEEE Micro, 2023
Vijay Janapa Reddi, Boris Murmann
openaire   +1 more source

Software frameworks for TinyML

Bharat S Chaudhari, Marco Zennaro
exaly   +2 more sources

TMM-TinyML

Proceedings of the 28th Annual International Conference on Mobile Computing And Networking, 2022
Bharath Sudharsan, Sonu Prasad, Dan Jose, John G. Breslin   +3 more
openaire   +1 more source