機器學習已經發展多年，至今仍然持續進行多方研究，例如深度學習。 然而，隨著硬體的革新，傳統的機器學習算法可能無法在新型態的硬體上獲得最佳效率。 根據不同的硬體特性，本文提出了一種現代的系統架構來提高隨機森林算法的性能。基於相變化記憶體，我們發現讀寫操作之間的延遲存在著顯著的差異。因此，我們除了設置檢查點以防止不必要的寫入並且使用讀取來替換寫入的動作。 接下來，考慮到記憶體的讀取局部性，通過不同的實作方式實現出更適合新型態記憶體的記憶體配置。實驗顯示，我們所提出的系統架構最多可以提高隨機森林演算法70％的效能。

Machine learning has been accessible for a long time and is still a trend to keep developing, such as deep learning. Nevertheless, with the upgrade of hardware, the traditional algorithm of machine learning might no longer get the best efficiency. According to the different features of new generation hardware, this paper proposes a modern architecture to enhance the performance of the random forest algorithm. Based on phase change memory, we found the discrepancy on latency between read and write operations. Therefore, we set checkpoints to prevent unnecessary write operations and replace write with read operations. Next, with considering to read locality, modify the implementation by a different perspective. The results show that the proposed design can improve performance by 70%.

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