The Internet of Things (IoT) has created a ubiquitously connected world powered by a
multitude of wired and wireless sensors generating a variety of heterogeneous data over …

Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of
Things (IoT) applications and services, spanning from recommendation systems and speech …

Despite existing work in machine learning inference serving, ease-of-use and cost efficiency
remain challenges at large scales. Developers must manually search through thousands of …

The difficulty of deploying various deep learning (DL) models on diverse DL hardware has
boosted the research and development of DL compilers in the community. Several DL …

Multi-Access Edge Computing (MEC) attracts much attention from the scientific community
due to its scientific, technical, and commercial implications. In particular, the European …

As the application of deep learning continues to grow, so does the amount of data used to
make predictions. While traditionally big-data deep learning was constrained by computing …

The ever-growing advances of deep learning in many areas including vision,
recommendation systems, natural language processing, etc., have led to the adoption of …

Collaborative inference (co-inference) accelerates deep neural network inference via
extracting representations at the device and making predictions at the edge server, which …

Benefiting from the advance of deep learning (DL) technology, Internet-of-Things (IoT)
devices and systems are becoming more intelligent and multifunctional. They are expected …

On-device ML introduces new security challenges: DNN models become white-box
accessible to device users. Based on white-box information, adversaries can conduct …