Today's data-centric applications demand cache architectures that can scale with growing workloads while maintaining high performance and energy efficiency. Fundamental issues such as excessive area consumption and leakage power are increasingly challenging traditional SRAM-based caches, thereby motivating the exploration of non-volatile alternatives. Among these, racetrack memory (RTM) stands out due to its remarkable storage density, achieved through nanowires hosting sequential magnetic domains that can be manipulated via domain wall or skyrmion techniques. Despite its advantages, racetrack memory's inherent serialized access introduces considerable shift overhead, leading to elevated energy consumption and latency. In this paper, we analyze the placement strategies in conventional RTM-based last-level caches and identify that current methods trigger redundant shift operations as a shift intended to read one block fails to preposition other blocks subsequently accessed. To resolve this, we introduce an innovative data placement and replacement scheme that intelligently groups correlated blocks, ensuring that a single shift not only retrieves the target block but also aligns subsequent blocks closer to the access port. Our simulation results using the gem5 simulator and the SPEC CPU2017 benchmarks reveal that our scheme reduces shift overhead by 49.0% and cache energy consumption by 35.9% with negligible performance impact. In addition, this scheme exhibits robust scalability to longer nanowire tracks for higher cache density.

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