Online thermal control methods for multiprocessor systems
ACM Transactions on Design Automation of Electronic Systems (TODAES) - Special section on adaptive power management for energy and temperature-aware computing systems
Proceedings of the Conference on Design, Automation and Test in Europe
Dynamic voltage and frequency scaling for shared resources in multicore processor designs
Proceedings of the 50th Annual Design Automation Conference
Dynamic power management for multidomain system-on-chip platforms: An optimal control approach
ACM Transactions on Design Automation of Electronic Systems (TODAES) - Special Section on Networks on Chip: Architecture, Tools, and Methodologies
In-network monitoring and control policy for DVFS of CMP networks-on-chip and last level caches
ACM Transactions on Design Automation of Electronic Systems (TODAES) - Special Section on Networks on Chip: Architecture, Tools, and Methodologies
Online learning of timeout policies for dynamic power management
ACM Transactions on Embedded Computing Systems (TECS)
Unified reliability estimation and management of NoC based chip multiprocessors
Microprocessors & Microsystems
A generic FPGA prototype for on-chip systems with network-on-chip communication infrastructure
Computers and Electrical Engineering
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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 resources allocation, as well as 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 (about 70%) can be achieved at run-time while running a variety of benchmark applications.