The emerging class of wide-area streaming analytics faces the challenge of scarce and
variable WAN bandwidth. Non-adaptive applications built with TCP or UDP suffer from …

Over the last decade, machine learning (ML) and deep learning (DL) algorithms have
significantly evolved and been employed in diverse applications, such as computer vision …

An increasing number of software applications incorporate runtime Deep Neural Networks
(DNNs) to process sensor data and return inference results to humans. Effective deployment …

Modern software should satisfy multiple goals simultaneously: it should provide predictable
performance, be robust to failures, handle peak loads and deal seamlessly with unexpected …

Autonomous micromobility systems (AMS) such as low-speed minicabs and robots are
thriving. In AMS, multiple Deep Neural Networks execute in parallel on heterogeneous AI …

The revolution of deep neural networks (DNNs) is enabling dramatically better autonomy in
autonomous driving. However, it is not straightforward to simultaneously achieve both timing …

Deep neural networks (DNNs) used in computed vision have become widespread
techniques commonly used in autonomous embedded systems for applications such as …

Recurrent neural networks (RNNs) are well-suited to the sequential inference tasks often
found in embedded sensing systems. While RNNs have displayed high accuracy on many …

The increasing demand for cloud-based services, such as big data analytics and online e-
commerce, leads to rapid growth of large-scale internet data centers. In order to provide …

Web developers face the stringent task of designing informative web pages while keeping
the page-load time low. This task has become increasingly challenging as most web …