Data-Driven Structured Thermal Modeling for COTS Multi-core Processors

Thermal awareness is increasingly important for real-time systems deployed in harsh environments. As high chip temperature can cause frequency throttling or shutdown of processor cores at unexpected times, many real-time scheduling techniques have been developed to ensure continuous, fail-safe operation of safety-critical tasks with stringent timing constraints. However, their practical use remains largely limited due to the fact that it is extremely difficult to obtain a precise thermal model of commercial processors without using special measurement instruments or access to proprietary information, such as the power traces of micro-architectural units and detailed floorplans.In this paper, we propose a data-driven structured thermal modeling scheme that is directly applicable to commercial off-the-shelf multi-core processors used in real-time embedded systems. By using a small number of thermal profiles obtained from on-chip temperature sensors, our scheme can accurately predict the processor operating temperature under dynamic real-time workloads at various CPU frequencies and ambient conditions. The thermal model derived from our scheme is fast to converge and robust against different sources of errors. Our scheme is non-intrusive, meaning that it does not require changes to the software code or the hardware packaging of the target system. Furthermore, our scheme can estimate the relative power consumption of the processor for a given workload and clock frequency level. Experimental results from a multi-core ARM platform indicate that our scheme estimates the operating temperature with a maximum error of 2.5% while the latest prior work results in 23% error. This highly accurate modeling enables us to obtain the maximum achievable processor utilization that does not cause a thermal safety violation.