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 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 …

The proliferation of sensing technologies such as sensors has resulted in vast amounts of
time-series data being produced by machines in industrial plants and factories. There is …

The demand to process vast amounts of data generated from state-of-the-art high resolution
cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such …

Abstract Artificial Intelligence of Things (AIoT) is an emerging area of interest, and this can
be used to obtain knowledge and take better decisions in the same Internet of Things (IoT) …

Abstract In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained
significant interest from both, industry and academia. Notably, conventional ML techniques …

In recent years, ML (Machine Learning) models that have been trained in data centers can
often be deployed for use on edge devices. When the model deployed on these devices …