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 …

Generative tasks, such as text generation and question answering, hold a crucial position in
the realm of mobile applications. Due to their sensitivity to privacy concerns, there is a …

Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine
valuable knowledge in edge computing (EC) systems. To boost the performance of AI …

In recent years, Deep Neural Network (DNN) has been increasingly adopted by a wide
range of time-critical applications running on edge platforms with heterogeneous …

Deep neural networks (DNNs) have been widely adopted for various mobile inference tasks,
yet their ever-increasing computational demands are hindering their deployment on …

Video, as a key driver in the global explosion of digital information, can create tremendous
benefits for human society. Governments and enterprises are deploying innumerable …

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 …

On-device Large Language Models (LLMs) are revolutionizing mobile AI, enabling
applications such as UI automation while addressing privacy concerns. Currently, the …

Edge-cloud jobs are rapidly prevailing in many application domains, posing the challenge of
using both resource-strenuous edge devices and elastic cloud resources. Efficient resource …

Mobile devices contribute more than half of the world's web traffic, providing massive and
diverse data for powering various federated learning (FL) applications. In order to avoid the …