Reducing energy consumption in multi-processor systems-on-chip (MPSoCs) where communication happens via the network-on-chip (NoC) approach calls for multiple voltage/frequency island (VFI)-based designs. In turn, such multi-VFI architectures need efficient, robust and accurate run-time control mechanisms that can exploit the workload characteristics in order to save power. Despite being tractable, the linear control models for power management cannot capture some important workload characteristics (e.g., fractality, non-stationarity) observed in heterogeneous NoCs. If ignored, such characteristics lead to inefficient communication and resource allocation, as well as to high power dissipation in MPSoCs. To mitigate such limitations, we propose a new paradigm shift from power optimization based on linear models to control approaches based on fractal-state equations. As such, our approach is the first to propose a controller for fractal workloads with precise constraints on state and control variables and specific time bounds. Our results show that significant power savings can be achieved at run-time while running a variety of benchmark applications.
Paul Bogdan received his Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh. He is a Post-Doctoral Fellow in the Electrical and Computer Engineering Department at Carnegie Mellon University. He was awarded the 2012 A.G. Jordan Award from the Electrical and Computer Engineering Department, Carnegie Mellon University for outstanding Ph.D. thesis and service, the 2012 Best Paper Award from the Networks-on-Chip Symposium (NOCS), the 2012 D.O. Pederson Best Paper Award from IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, the 2012 Best Paper Award from the International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), the 2012 Best in Session Award at TECHCON and the 2009 Roberto Rocca Ph.D. Fellowship. His research interests include performance analysis and design methodologies for multicore systems, the theoretical foundations of cyber-physical systems, the modeling and analysis of bio-inspired computing, and the applications of statistical physics to biological systems and regenerative medicine.