Accurate temperature estimation using noisy thermal sensors

Multicore SOCs rely on runtime thermal measurements using on-chip sensors for DTM. In this paper we address the problem of estimating the actual temperature of on-chip thermal sensor when the sensor reading has been corrupted by noise. Thermal sensors are prone to noise due to fabrication randomness, V_DD fluctuations etc. This causes discrepancy between actual temperature and the one predicted by thermal sensor. Our experiments estimate this variation to be around 30%. In this paper we present a statistical methodology for predicting the actual temperature for a given sensor reading. We present two techniques: single sensor prediction and multi-sensor prediction. The latter tries to estimate the actual temperature for each sensor (of the many on-chip sensors) simultaneously while exploiting the correlations between temperature and noise of different sensors. When the underlying randomness follows a Gaussian characteristic, we present optimal schemes of estimating the expected temperature. We also present heuristic schemes for the case where the Gaussian assumption fails to hold. The experiments showed that using our estimation schemes the RMS error can be reduce as much as 67% as compared to blindly trusting the sensors to be noise free.