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 …

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

Object detection is a fundamental building block of video analytics applications. While
Neural Networks (NNs)-based object detection models have shown excellent accuracy on …

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

Concurrent inference execution on heterogeneous processors is critical to improve the
performance of increasingly heavy deep learning (DL) models. However, available …

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 …

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 …

Today, an increasing number of Adaptive Deep Neural Networks (AdNNs) are being used
on resource-constrained embedded devices. We observe that, similar to traditional software …

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 …