A stochastic local hot spot alerting technique

With the increasing levels of variability in the behavior of manufactured nano-scale devices and dramatic changes in the power density on a chip, timely identification of hot spots on a chip has become a challenging task. This paper addresses the questions of how and when to identify and issue a hot spot alert. There are important questions since temperature reports by thermal sensors may be erroneous, noisy, or arrive too late to enable effective application of thermal management mechanisms to avoid chip failure. This paper thus presents a stochastic technique for identifying and reporting local hot spots under probabilistic conditions induced by uncertainty in the chip junction temperature and the system power state. More specifically, it introduces a stochastic framework for estimating the chip temperature and the power state of the system based on a combination of Kalman filtering (KF) and Markovian decision process (MDP) model. Experimental results demonstrate the effectiveness of the framework and show that the proposed technique alerts about thermal threats accurately and in a timely fashion in spite of noisy or sometimes erroneous readings by the temperature sensor.