斯格明子賽道記憶體（Sky-RM）因其高存儲密度的潛力而備受關注， 尤其是對於資料儲存需求不斷增長的現今尤為重要。Sky-RM 利用斯格明子 的奈米尺度尺寸，與傳統記憶體技術相比顯著減少了物理空間需求。然而， 由於其獨特的特性，包括位移、生成和消除，如果缺乏有效的演算法來管 理這些操作，Sky-RM 的整體性能可能顯著低於傳統的動態隨機存取記憶體 （DRAM）。 因此，本論文致力於開發一種具有高並行性、低延遲、低能耗和高空 間利用率的資料擺放方式。此外，我們將我們的方法應用到基於隨機森林 的機器學習框架中，以檢驗是否實現了高平行度以及延遲和能耗的減少。;Skyrmion-based Racetrack Memory(Sky-RM) has gained attention due to its potential to offer high storage density, which is increasingly critical as the demand for data storage continues to grow. Sky-RM leverages the nanoscale size of skyrmions, allowing for a significant reduction in physical space requirements compared to traditional memory technologies. However, due to its unique characteristics, including shifting, generating, and eliminating, Sky-RM can exhibit significantly poorer overall performance compared to traditional DRAM if it lacks a robust algorithm to manage these operations effectively. Therefore, this paper is dedicated to develop a high parallelism, low latency, low energy consumption and high space utilization placement strategy. Furthermore, we integrate our method onto the random forest based machine learning framework to see whether high parallelism and reductions in latency and energy consumption have been achieved.
