A Hybrid Memory Data Placement Strategy for Edge Computing
Binghui Lin, Jianxun Zhang, Xinyu Qiao · 2024
With the booming development of fields such as cloud computing, big data, and artificial intelligence, the productivity of data have experienced explosive growth, prompting the expansion of edge computing. However, as data-intensive applications continue to increase, memory systems based solely on Dynamic Random Access Memory (DRAM) are no longer able to meet the demands of edge computing for high memory footprints, low access latency, and low energy consumption. In this paper, we organize DRAM as a cache for Non-Volatile Memory (NVM) and propose a utility-based hybrid memory data placement (UDP-HM) strategy, to address energy efficiency issues. UDP-HM places data in a hybrid memory system by calculating page utility. A series of simulation experiments are conducted to verify and evaluate the proposed hybrid memory data placement strategy. Under different workload intensities, compared to UH-MEM and RBLA, UDP-HM shows an average increase of 8.71% and 9.21% respectively in DRAM cache read/write operations, an average increase of 10.18% and 13.94% respectively in latency, an average decrease of 35.24% and 37.18% respectively in energy consumption, and an average decrease of 26.48% and 31.68% respectively in energy-delay product.