DNNs are becoming less and less over-parametrised due to recent advances in efficient
model design, through careful hand-crafted or NAS-based methods. Relying on the fact that …

Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual
apps spanning from on-demand movies and 360° videos to video-conferencing and live …

Federated Learning (FL) has been gaining significant traction across different ML tasks,
ranging from vision to keyboard predictions. In large-scale deployments, client heterogeneity …

Despite the soaring use of convolutional neural networks (CNNs) in mobile applications,
uniformly sustaining high-performance inference on mobile has been elusive due to the …

Hyperparameter optimization (HPO) is a necessary step to ensure the best possible
performance of Machine Learning (ML) algorithms. Several methods have been developed …

With smartphones' omnipresence in people's pockets, Machine Learning (ML) on mobile is
gaining traction as devices become more powerful. With applications ranging from visual …

Deep neural networks (DNNs) have become ubiquitous techniques in mobile and
embedded systems for applications such as image/object recognition and classification. The …

Semantic segmentation arises as the backbone of many vision systems, spanning from self-
driving cars and robot navigation to augmented reality and teleconferencing. Frequently …

In recent years, deep learning (DL) models have demonstrated remarkable achievements
on non-trivial tasks such as speech recognition, image processing, and natural language …

Deep learning models are increasingly deployed to edge devices for real-time applications.
To ensure stable service quality across diverse edge environments, it is highly desirable to …