Abstract Machine learning has become an indispensable part of the existing technological
domain. Edge computing and Internet of Things (IoT) together presents a new opportunity to …

The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …

On-device training enables the model to adapt to new data collected from the sensors by
fine-tuning a pre-trained model. Users can benefit from customized AI models without having …

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 …

The rapid emergence of low-power embedded devices and modern machine learning (ML)
algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks …

Tiny Machine Learning (TinyML), a rapidly evolving edge computing concept that links
embedded systems (hardware and software) and machine learning, with the purpose of …

Machine Learning (TinyML) is an upsurging research field that proposes to democratize the
use of Machine Learning and Deep Learning on highly energy-efficient frugal …

Industrial assets often feature multiple sensing devices to keep track of their status by
monitoring certain physical parameters. These readings can be analyzed with machine …

In the last few years, research and development on Deep Learning models & techniques for
ultra-low-power devices–in a word, TinyML–has mainly focused on a train-then-deploy …

TinyML has the potential to be a huge enabler of smart sensor nodes for fault diagnosis of
machines by embedding powerful machine learning algorithms in low-cost edge devices …