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 …

Domain-specific systems-on-chip, a class of heterogeneous many-core systems, is
recognized as a key approach to narrow down the performance and energy-efficiency gap …

Multicore platforms are becoming the dominant trend in designing Mixed-Criticality Systems
(MCSs), which integrate applications of different levels of criticality into the same platform. A …

In recent decades, the rapid advances in information technology have promoted a
widespread deployment of medical cyber-physical systems (MCPS), especially in the area of …

The increasing complexity and size of design space poses significant challenges for
integrated circuit (IC) design. This article discusses the potential of machine learning (ML) …

Embedded system applications often require guarantees regarding non-functional
properties when executed on a given MPSoC platform. Examples of such requirements …

Current multi/many-core systems spend large amounts of time and power transmitting data
across on-chip interconnects. This problem is aggravated when data-intensive applications …

Many power management algorithms demand accurate and fine-grained runtime
estimations of dynamic core power. In the absence of fine-grained power sensors, model …

When designing embedded systems, engineers have to consider non-functional
requirements, such as real-time or energy consumption constraints. To enforce or counteract …