Machine Learning-Based Prefetching for SCM Main Memory System

Demand for in-memory data processing of large-scale data is expanding, and expectations for storage-class memories (SCMs) are increasing accordingly. SCM achieves low standby power and higher density compared to DRAM. However, SCM is relatively slower than DRAM and requires more dynamic power. Therefore, it is necessary to improve speeds and reduce power usage by SCM by performing memory hierarchical control such as power-efficient prefetch control according to application memory access characteristics. However, such memory hierarchical control is complicated, making it difficult to determine an optimal memory control. Therefore, we propose an auto-tuning framework for dynamically predicting optimal memory control for SCM main memory system using machine learning based on system-level time series performance data. In this paper, we describe application of the proposed framework to prefetch control and evaluate the feasibility of power-efficient prefetch control. The results confirm automatic generation of prediction models reflecting domain knowledge of computer systems, allowing high-speed low-power real-time memory control.