A Machine Learning Approach for Improving Power Efficiency on Clustered Multi-Processor System

Modern embedded systems have adopted the clustered Chip Multi-Processor (CMP) paradigm in conjunction with dynamic frequency scaling techniques for improving application performance and power consumption. However, applications suffer from performance saturation due to resource contention. Thus, after a certain point, any frequency increase results only in power overheads without any performance gains. In this work, we present a run-time manager that focuses on power efficiency improvement for clustered CMPs. Specifically, it monitors the activity of concurrently executing applications and utilizes neural networks to select an appropriate frequency that keeps performance high, while reducing power consumption. Experimental results on the Odroid-XU3 board show that the proposed methodology improves power efficiency (MIPS/Watt) by 23% compared to Linux's performance governor with a negligible performance drop of only 3%.