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.
Seyedmehdi Hosseinimotlagh, Daniel Enright, Christian R. Shelton, and Hyoseung Kim (2021). "Data-Driven Structured Thermal Modeling for COTS Multi-Core Processors." IEEE Real-Time Systems Symposium. |
@inproceedings{HosEnrSheKim21, author = "Seyedmehdi Hosseinimotlagh and Daniel Enright and Christian R. Shelton and Hyoseung Kim", title = "Data-Driven Structured Thermal Modeling for {COTS} Multi-Core Processors", booktitle = "{IEEE} Real-Time Systems Symposium", booktitleabbr = "{RTSS}", year = 2021, }