Stochastic dynamic power and thermal management techniques for multicore systems

UnrestrictedWith the progress in today’s semiconductor technology, the chip density and operating frequency have increased rapidly, making the power consumption in digital circuits a major concern for VLSI designers. Furthermore, as the nanometer technology is approaching the regime of randomness with variability in the behavior of silicon structure, improving the accuracy of power optimization techniques under increasing levels of process, voltage, and temperature (PVT) variations is becoming a critical task. In this dissertation, a stochastic dynamic power management (DPM) framework is presented to improve the accuracy of decision-making strategy for power management under probabilistic conditions induced by the PVT variations. Subsequently, an adaptive DPM technique is presented by constructing a machine-learning based power manager to improve the quality of decision-making strategy and to reduce the overhead of the power manager which has to repetitively determine and assign voltage-frequency settings for each core in multicore systems. Finally, the focus of this dissertation is shifted to thermal management techniques, since thermal control is also becoming a first-order concern due to the increased power density as well as the increasing vulnerability of the system. A technique is presented to identify and report local hot spots under probabilistic conditions induced by uncertainty in the chip junction temperature and the system power state. Lastly, an abstract model of a thermally-managed system with stochastic processes is introduced, followed by a stochastic thermal management technique