Learning-based BTI stress estimation and mitigation in multi-core processor systems

With the increasing demand of designing a reliable processing devices, the issue of CMOS ageing is jeopardising the industry of digital devices. Many studies has been cover this area for modelling the ageing behaviour at the device level or developing ageing sensors for on-line delay detection at the system level. However, we are presenting a method to estimate the ageing stresses (e.g. Temperature, Ageing Stress Activity) rather than the modelling ageing (performance degradation) itself. The purpose for estimating the ageing stress is to optimise the system utilisation with the minimisation of ageing stress. In multicore processors, the existence of more than one source of ageing stress is higher than single core processor but the optimisation space is higher as well along with the temperature and power optimisation. In this paper, we have modelled the ageing stress from the application level using machine learning techniques to train data extracted from high level workloads ( e.g. parsec and splash2 benchmarks) on four cores processor from Xeon. The ageing stress model is able to estimate the ageing stress with 0.1% error and is able to proactively reduce the ageing stress by 50%.