BLOwing Trees to the Ground: Layout Optimization of Decision Trees on Racetrack Memory
Hosted in Virtual Platform
Embedded Memory, Storage and Networking
DescriptionModern embedded-systems integrate machine learning algorithms. In resource constrained setups, execution has to be optimized for execution time and energy. In order to access data in RTM, it needs to be shifted to the access port.
We propose a novel domain specific approach for placing decision trees in RTMs. We reduce the total amount of shits by exploiting the tree structure. We prove that the theoretical optimal decision tree placement is at most 4× better in terms of shifts than our proposed approach. Throughout extensive experiments, we show that our method outperforms the state-of-the-art methods.