| dc.description.abstract | With the demand of the high memory density and fast memory load/store performance, Skyrmion racetrack memory emerges as a leading contender to replace current DRAM technology in main memory applications. However, the distinctive features of skyrmion racetrack memory, including its ability to shift, generate, and destroy skyrmion elements, diverge significantly from traditional DRAM. Without reevaluating conventional algorithms and data structures to align with the unique properties of skyrmion-based main memory, system performance becomes poor. The swapping operation, fundamental in various algorithms like sorting, searching, graph, and machine learning, poses challenges when directly applied to skyrmion-based main memory. The conventional approach can lead to heightened power consumption from frequent skyrmion element creation or suboptimal system performance due to frequent shifting operations. To address this, our paper proposes a skyrmion-friendly approach, introducing a swappingfriendly racetrack memory architecture to concurrently minimize energy consumption and access latency. Furthermore, we integrate this solution with a decision tree-based machine learning framework, recognizing the pivotal role of the swapping process in frequent execution during decision tree construction. Our experiments yield encouraging results, demonstrating reductions in energy consumption and access latency. | en_US |