As the power density increases exponentially, the runtime regulation of operating temperature by dynamic thermal management (DTM) becomes necessary. This paper proposes two novel approaches to the thermal analysis at the chip architecture level for efficient DTM. The first method, i.e., thermal moment matching with spectrum analysis, is based on observations that the power consumption of architecture-level modules in microprocessors running typical workloads presents a strong nature of periodicity. Such a feature can be exploited by fast spectrum analysis in the frequency domain for computing steady-state. response. The second method, i.e., thermal moment matching based on piecewise constant power inputs, is based on the observation that the average power consumption of architecture-level modules in microprocessors running typical workloads determines the trend of temperature variations. As a result, using piecewise constant average power inputs can further speed up the thermal analysis. To obtain transient temperature changes due to the initial condition and constant/average power inputs, numerically stable moment matching methods, with enhanced pole searching are carried out to speed up online temperature tracking with high accuracy and low overhead. The resulting thermal analysis algorithm has a linear time complexity in runtime setting when the average power inputs are applied. Experimental results show that the resulting thermal analysis algorithms lead to 10 X -100 X speedup over the traditional integration-based transient analysis with small accuracy loss.
