Temperature and process-aware performance monitoring and compensation for an ULP multi-core cluster in 28nm UTBB FD-SOI technology

Abstract

Environmental temperature variations, as well as process variations, have a detrimental effect on performance and reliability of embedded systems implemented with deep-sub micron technologies. This sensitivity significantly increases in ultra-low-power (ULP) devices that operate in near-threshold, due to the magnification of process variations and to the strong thermal inversion that affects advanced technology nodes. Supporting an extended range of reverse and forward body-bias, UTBB FD-SOI technology provides a powerful knob to compensate for such variations. In this work we propose a methodology to efficiently compensate, at run-time, these variations. The proposed method exploits on-line performance measurements by means of Process Monitoring Blocks (PMBs) coupled with on-chip low-power Body Bias Generators. We characterize the response of the PMBs versus the maximum achievable frequency of the system, deriving a predictive model able to estimate such frequency with an error of 3%. We apply this model to compensate Temperature-induced performance variations, estimating the maximum frequency with an error of 7%; we eliminate the error by adding an appropriate body-bias margin resulting in a worst case global power consumption overhead of 5%. As further improvement, we generalize the methodology to compensate also process variations, obtaining an error of 28% on the estimated maximum performance and compensating this error with an overhead of 17% on the global power consumption.