Big Data for LITTLE Cores: Combining Learning and Control for Mobile Energy Efficiency

Nikita Mishra; Connor Imes; Huazhe Zhang; John D Lafferty; Henry Hoffmann. 20 September, 2016.
Communicated by Henry Hoffmann.


Mobile systems must deliver performance to interactive applications while simultaneously conserving resources to extend battery life. There are two central challenges to meeting these conflicting goals: (1) the complicated optimization spaces arising from hardware heterogeneity and (2) dynamic changes in application behavior and resource availability. Machine learning techniques handle complicated optimization spaces, but do not incorporate models of system dynamics; control theory provides formal guarantees of dynamic behavior, but struggles with non-linear system models. In this paper, we propose CALOREE, a combination of learning and control techniques to meet performance requirements on heterogeneous devices in unpredictable environments. CALOREE combines a hierarchical Bayesian model (HBM) with a lightweight control system (LCS). The HBM runs remotely, learning customized performance/power models. The LCS runs on the mobile system and tunes resource usage to meet performance goals. The Performance Hash Table (PHT) is the interface between the two and allows the LCS to apply the learned models in constant time. We test CALOREE’s ability to manage ARM big.LITTLE systems. Compared to existing learning and control methods, CALOREE delivers more reliable performance – only 2% error compared to 4.5-5.4% for learning and 4.7% for control – and lower energy – within 7% of optimal on average as compared to 25-52% for learning and 26% for control. Furthermore, we demonstrate CALOREE’s ability to meet performance and energy goals in dynamic systems with phase changes and multiple applications running on the same system.

Original Document

The original document is available in PDF (uploaded 20 September, 2016 by Henry Hoffmann).