Results 91 to 100 of about 374 (124)

TinyML in Africa: Opportunities and Challenges

2021 IEEE Globecom Workshops (GC Wkshps), 2021
Samson Otieno Ooko, Marvin Muyonga Ogore, Jimmy Nsenga, Marco Zennaro   +3 more
openaire   +1 more source

TinyML Acceleration with MAX78000

The advancement of edge devices equipped with specialized hardware accelerators has brought the deployment and execution of Deep Neural Network (DNN) models nearer to users and real-world sensor systems. This paper investigates the potential of the MAX78000 microcontroller in accelerating Tiny Machine Learning applications, which require real-time ...
Dabbous A., Lazzaroni L., Bellotti F., Presentacion S. M., Pighetti A., Berta R.   +5 more
openaire   +1 more source

Work in Progress: Linear Transformers for TinyML

2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)
We present the WaveFormer, a neural network architecture based on a linear attention transformer to enable long sequence inference for TinyML devices. Waveformer achieves a new state-of-the-art accuracy of 98.8 % and 99.1 % on the Google Speech V2 keyword spotting (KWS) dataset for the 12 and 35 class problems with only 130 kB of weight storage ...
Scherer M., Cioflan C., Magno M., Benini L.   +3 more
openaire   +1 more source

TinyML for EEG Decoding on Microcontrollers

2023 IEEE International Symposium on Circuits and Systems (ISCAS), 2023
Antonios Tragoudaras, Charalampos Antoniadis, Yehia Massoud   +2 more
openaire   +1 more source

TinyML Workshop

2023 IEEE 16th Dallas Circuits and Systems Conference (DCAS), 2023
openaire   +1 more source

Intelligence at the Extreme Edge: A Survey on Reformable TinyML

ACM Computing Surveys, 2023
Visal Rajapakse, Ishan Karunanayake, Nadeem Ahmed   +2 more
exaly  

Towards energy-aware tinyML on battery-less IoT devices

Internet of Things (Netherlands), 2023
Adnan Šabović, Michiel Aernouts, Jaron Fontaine   +2 more
exaly  

Why TinyML? Exploring the Reasons why TinyML is used for Real-World Problems

Signal and Image Processing
Machine Learning (ML) and especially its application to cyber-physical systems is an uprising field of research. Many approaches on how to leverage the power of ML even in small devices have been published and applied in recent years, forming the field of TinyML. While TinyML has been promising several benefits such as cost-reduction, privacy and more,
openaire   +1 more source

TinyML-enabled edge implementation of transfer learning framework for domain generalization in machine fault diagnosis

Expert Systems With Applications, 2023
Supriya Asutkar, Chaitravi Chalke, Kajal Shivgan   +2 more
exaly  

Synergy of Patent and Open-Source-Driven Sustainable Climate Governance under Green AI: A Case Study of TinyML

Sustainability, 2023
Tao Li
exaly