27th International Conference on Real-Time Networks and Systems November 6-8, 2019 -Toulouse/France
|Date: Thursday, November 7,|
Author: Marko Bertogna (University of Modena, Italy)
Title: A View on Future Challenges for the Real-Time Community (slides)
Marko Bertogna is Full Professor at the University of Modena (Italy), where he leads the High-Perfomance Real-Time Systems Laboratory (HiPeRT Lab). His main research interests are in High-Performance Real-Time systems, especially based on multi- and many-core devices, Autonomous Driving and Industrial Automation systems, with particular relation to related timing and safety requirements. Previously, he was Assistant Professor at the Scuola Superiore Sant’Anna of Pisa, working at the Real-Time Systems Lab since 2003. He graduated magna cum laude in Telecommunication Engineering at the University of Bologna in 2002. From 2001 to 2002, he worked on integrated optical devices at the Technical University of Delft, The Netherlands. In 2006, he visited the University of North Carolina at Chapel Hill, working with prof. Sanjoy Baruah on scheduling algorithms for single and multicore real-time systems. In 2008, he received a PhD in Computer Sciences from the Scuola Superiore Sant’Anna of Pisa, with a dissertation on Real-Time Systems for Multicore Platforms, awarded as the best scientific PhD thesis discussed at Scuola Superiore Sant’Anna in 2008 and 2009.
Latest technological innovations in embedded systems-on-chip are fostering a new set of applications that aim at replacing human activities, integrating complex parallel workloads with safety-critical real-time tasks: self-driving vehicles, vision-based industrial automation, autonomous robots, etc. The real-time community is slowly adapting its research targets moving beyond classic models and solutions to capture the new research challenges posed by modern platforms.
In this talk, I will try to motivate the technological reasons behind new theoretical and practical problems that our community will need to face, identifying potential research directions to restore predictability in the execution of real-time workloads on embedded super-computing platforms. I’ll explain why these problems will become ever more important in the upcoming years, offering our community the chance of significantly increasing its industrial impact.