Machine Learning (ML) has demonstrated great promise in various fields, eg, self-driving,
smart city, which are fundamentally altering the way individuals and organizations live, work …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep
Neural Networks (DNNs) have quickly attracted widespread attention. However, it is …

Despite the soaring use of convolutional neural networks (CNNs) in mobile applications,
uniformly sustaining high-performance inference on mobile has been elusive due to the …

Recent advances in artificial intelligence have driven increasing intelligent applications at
the network edge, such as smart home, smart factory, and smart city. To deploy …

Edge machine learning involves the deployment of learning algorithms at the network edge
to leverage massive distributed data and computation resources to train artificial intelligence …

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

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

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 …

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