The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of
Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware …

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

With the advent of Tiny Machine Learning (tinyML), it is increasingly feasible to deploy
optimized ML models on constrained battery-less Internet of Things (IoT) devices with …

Over-the-air federated learning (OTA-FL) is a distributed machine learning technique where
multiple devices collaboratively train a shared model without sharing their raw data with a …

TinyML/DL is a new subfield of ML that allows for the deployment of ML algorithms on low-
power devices to process their own data. The lack of resources restricts the aforementioned …

Abstract Trained Artificial Intelligence (AI) models are challenging to install on edge devices
as they are low in memory and computational power. Pruned AI (PAI) models are therefore …

Over the last years the rapid growth Machine Learning (ML) inference applications deployed
on the Edge is rapidly increasing. Recent Internet of Things (IoT) devices and …

Tiny Machine Learning (TinyML) and Transfer Learning (TL) are two widespread methods of
successfully deploying ML models to resource-starving devices. Tiny ML provides compact …

Embedded Machine Learning (ML) is increasingly pivotal in contemporary data-driven
applications, mainly when operating on tiny, resource-constrained devices where model …

This paper introduces a novel pruning method designed for transfer-learning models in
computer vision, leveraging XGBoost to enhance model efficiency through simultaneous …