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 …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

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 …

Abstract We introduce TensorFlow (TF) Micro, an open-source machine learning inference
framework for running deep-learning models on embedded systems. TF Micro tackles the …

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 …

Executing machine learning workloads locally on resource constrained microcontrollers
(MCUs) promises to drastically expand the application space of IoT. However, so-called …

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 TinyML paradigm proposes to integrate Machine Learning (ML)-based mechanisms
within small objects powered by Microcontroller Units (MCUs). This paves the way for the …

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 …

Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are
placed in various fields. Many of these devices are based on machine learning (ML) models …