Abstract: Advances in computer systems and their performance have played an important role in the ongoing AI revolution. A good example is high-performance GPU systems that have helped to enable the training of increasingly deep neural networks. Since then, the AI revolution has also acted as a source of inspiration for a lot of computer systems research focused on either better handling of AI-based workloads or deploying AI methods to design and implement better systems. This trend can also be seen in embedded computer systems, which are ubiquitous computing systems that can be found in many application domains, such as consumer electronics, industrial systems, healthcare, automotive, and avionics. In this talk, I will highlight a few of such research efforts in the area of neural-network model inference on resource-constrained edge devices as well as in the domain of applying AI techniques for improving the robustness of industrial cyber-physical systems.

Andy D. Pimentel is full professor at the University of Amsterdam where he chairs the Parallel Computing Systems group. His research centers around system-level modeling, simulation and exploration of (embedded) multicore and manycore computer systems with the purpose of efficiently and effectively designing and programming these systems. He has an MSc and PhD in computer science from the University of Amsterdam. He is a cofounder of the International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS). He has (co-)authored more than 130 scientific publications and is an Associate Editor of the Simulation Modelling Practice and Theory journal as well as the Journal of Signal Processing Systems. He served as the General Chair of the HIPEAC 2015 conference, as Local Organization Co-Chair of Embedded Systems Week 2015, and as Program Chair of CODES+ISSS in 2016 and 2017. Furthermore, he has served on the TPC of many leading (embedded) computer systems design conferences, such as DAC, DATE, CODES+ISSS, ICCD, ICCAD, FPL, and LCTES.