On-chip sensor-driven efficient thermal profile estimation algorithms

This article addresses the problem of chip-level thermal profile estimation using runtime temperature sensor readings. We address the challenges of: (a) availability of only a few thermal sensors with constrained locations (sensors cannot be placed just anywhere); (b) random chip power density characteristics due to unpredictable workloads and fabrication variability. Firstly we model the random power density as a probability density function. Given such statistical characteristics and the runtime thermal sensor readings, we exploit the correlation in power dissipation among different chip modules to estimate the expected value of temperature at each chip location. Our methods are optimal if the underlying power density has Gaussian nature. We give a heuristic method to estimate the chip-level thermal profile when the underlying randomness is non-Gaussian. An extension of our method has also been proposed to address the dynamic case. Several speedup strategies are carefully investigated to improve the efficiency of the estimation algorithm. Experimental results indicated that, given only a few thermal sensors, our method can generate highly accurate chip-level thermal profile estimates within a few milliseconds.