As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

Federated learning (FL) is an attractive distributed learning paradigm supporting real-time
continuous learning and client privacy by default. In most FL approaches, all edge clients …

Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …

Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
To support fast inference of on-device DL, DL libraries play a critical role as algorithms and …

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 …

Recent researchers have proposed adaptive inference methods with an early-exiting
mechanism, which stops the inference procedure of input if the prediction is with high …

Designing networks capable of attaining better performance with an increased inference
budget is important to facilitate generalization to harder problem instances. Recent efforts …

In today's landscape, smartphones have evolved into hubs for hosting a multitude of deep
learning models aimed at local execution. A key realization driving this work is the notable …

Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude
of industries, including consumer electronics, healthcare, and manufacturing, largely due to …

Multi-modal computing (M 2 C) has recently exhibited impressive accuracy improvements in
numerous autonomous artificial intelligence of things (AIoT) systems. However, this …