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Modern low power distributed systems tend to integrate machine learning algorithms. In resource-constrained setups, the execution of the models has to be optimized for performance and energy consumption. Racetrack memory (RTM) promises to achieve these goals by offering unprecedented integration density, smaller access latency, and reduced energy consumption. However, to access data in RTM, it needs to be  shifted  to the  access port  first. We investigate decision trees and develop placement strategies to reduce the total number of shifts in RTM. Decision trees allow profiling during training, resulting in tree paths&#x2019; access probabilities. We map tree nodes to RTM so that the total number of shifts is minimal. Concretely, we present two different placement approaches: 1) where tree nodes are closely packed and placed  uniformly  in a single RTM location and 2) where decision tree nodes are  decomposed to separate RTM blocks. We discuss theoretical cost models for both approaches, we formally prove an upper bound of   $4\times$4  &#x00D7;for the unified and an upper bound of   $12\times$12  &#x00D7;for the decomposed organization towards the optimal placement. We conduct a thorough experimental evaluation to compare our algorithms to the state-of-the-art placement strategies Our experimental evaluations show that the  unified  and   decomposed   solutions reduce the number of shifts by    $58.1\%$58  .  1  %and$80.1\%$80  .  1  %, respectively, leading to a    $53.8\%$53  .  8  %and$46.3\%$46  .  3  %reduction in the overall runtime and    $52.6\%$52  .  6  %and$61.7\%$61  .  7  %reduction in the energy consumption, compared to a naive baseline.

ROLLED: R acetrack Memory O ptimized L inear L ayout and E fficient D ecomposition of Decision Trees