Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …

On-device learning is a promising technique for emerging privacy-preserving machine
learning paradigms. However, through quantitative experiments, we find that commodity …

This paper proposes Mandheling, the first system that enables highly resource-efficient on-
device training by orchestrating mixed-precision training with on-chip Digital Signal …

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 …

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 …

Currently, most reliable and commercialized artificial pancreas systems for type 1 diabetes
are hybrid closed-loop systems, which require the user to announce every meal and its size …

The recent breakthroughs in machine learning (ML) and deep learning (DL) have catalyzed
the design and development of various intelligent systems over wide application domains …

Background This study presents a Robot‐Inspired Computer‐Assisted Adaptive Autism
Therapy (RoboCA3T) focusing on improving joint attention and imitation skills of children …

To preserve user privacy while enabling mobile intelligence, techniques have been
proposed to train deep neural networks on decentralized data. However, training over …

Web applications have increasingly adopted Deep Learning (DL) through in-browser
inference, wherein DL inference performs directly within Web browsers. The actual …