The Efficient Stream Processing lab focusses on addressing the key challenges in this design flow, i.e. developing adaptive programming languages for streaming applications, platform-aware compilers, and memory-centric architectures.

Efficient stream processing at the edge is critical for realizing a sustainable society. For example, intelligent camera systems monitor real time traffic with the objective to guide traffic flows thereby reducing traffic jams and pollution. Other systems in for example smart homes (building automation), health (vital signs monitoring), industry (predictive maintenance, quality control), agriculture (weather forecasting), leisure (crowd management) produce, often with regular intervals, large streams of data that need to be processed in real time. Sometimes processing in the cloud is feasible, but more often stringent timing and privacy constraints require processing of the data inside the edge devices. To deliver the required capabilities and the economic promise, research in novel design methods and architectures for efficient stream processing is critical.