The rapid development of deep learning algorithms, as well as innovative hardware
advancements, encourages multi-DNN workloads such as augmented reality applications …

Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …

Deep learning inference is increasingly run at the edge. As the programming and system
stack support becomes mature, it enables acceleration opportunities in a mobile system …

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 …

On-device large language models (LLMs) are catalyzing novel mobile applications such as
UI task automation and personalized email auto-reply, without giving away users' private …

The rapid growth of memory and computation requirements of large language models
(LLMs) has outpaced the development of hardware, hindering people who lack large-scale …

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 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 …

Federated learning (FL) is a promising and privacy-preserving distributed learning method
that is widely deployed on edge devices. However, in practical applications, the data …

With the proliferation of applications with machine learning (ML), the importance of edge
platforms has been growing to process streaming sensor, data locally without resorting to …