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Adaptive and autonomous thermal tracking for high performance computing systems

Many DTM schemes rely heavily on the accurate knowledge of the chip's dynamic thermal state to make optimal performance/ temperature trade-off decisions. This information is typically generated using a combination of thermal sensor inputs and various estimation schemes such as Kalman filter. A basic assumption used by such schemes is that the statistical characteristics of the power consumption do not change. This is problematic since such characteristics are heavily application dependent. In this paper, we first present autonomous schemes for detecting the change in the statistical characteristics of power and then propose adaptive schemes for capturing such new statistical parameters dynamically. This could enable accurate temperature estimation during runtime given dynamically changing power statistical states. Our schemes use a combination of hypothesis testing and residual whitening methods and can improve the accuracy by 67% as compared to the traditional non-adaptive schemes.