Predictive Dynamic Thermal and Power Management for Heterogeneous Mobile Platforms
Gaurav Singla1, Gurinderjit Kaur1, Ali K. Unver2 and Umit Y. Ogras1
1School of Electrical, Computer, and Energy Engineering, Arizona State University, USA
2Assembly & Test Technology Development, Intel Corporation, USA
Heterogeneous multiprocessor systems-on-chip (MPSoCs) powering mobile platforms integrate multiple asymmetric CPU cores, a GPU, and many specialized processors. When the MPSoC operates close to its peak performance, power dissipation easily increases the temperature, hence adversely impacts reliability. Since using a fan is not a viable solution for hand-held devices, there is a strong need for dynamic thermal and power management (DTPM) algorithms that can regulate temperature with minimal performance impact. This paper presents a DTPM algorithm based on a practical temperature prediction methodology using system identification. The DTPM algorithm dynamically computes a power budget using the predicted temperature, and controls the types and number of active processors as well as their frequencies. Experiments on an octa-core big.LITTLE processor and common Android apps demonstrate that the proposed technique predicts temperature within 3% accuracy, while the DTPM algorithm provides around 6× reduction in temperature variance, and as large as 16% reduction in total platform power compared to using a fan.
Full Text (PDF)