Edge artificial intelligence (AI) is an innovative computing paradigm that aims to shift the
training and inference of machine learning models to the edge of the network. This paradigm …

Artificial Intelligence (AI) fosters enormous business opportunities that build and utilize
private AI models. Implementing AI models at scale and ensuring cost-effective production of …

We consider a resource-constrained Edge Device (ED), such as an IoT sensor or a
microcontroller unit, embedded with a small-size ML model (S-ML) for a generic …

The Hierarchical Inference (HI) paradigm has recently emerged as an effective method for
balancing inference accuracy, data processing, transmission throughput, and offloading …

We consider a resource-constrained Edge Device (ED), such as an IoT sensor or a
microcontroller unit, embedded with a small-size ML model (S-ML) for a generic …

On-device inference holds great potential for increased energy efficiency, responsiveness,
and privacy in edge ML systems. However, due to less capable ML models that can be …

Hierarchical Inference (HI) has emerged as a promising approach for efficient distributed
inference between end devices deployed with small pre-trained Deep Learning (DL) models …

This paper presents ExPECA, an edge computing and wireless communication research
testbed designed to tackle two pressing challenges: comprehensive end-to-end …

Deep learning (DL) applications have rapidly evolved to address increasingly complex tasks
by leveraging large-scale, resource-intensive models. However, deploying such models on …

In the era of widespread intelligent frameworks and models, we are often surrounded by
systems that house multiple models for varied task-specific predictions. Given the general …