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arXiv · 2501.17428

WCDT: Systematic WCET Optimization for Decision Tree Implementations

Nils Hölscher·Christian Hakert·Georg von der Brüggen·Jian-Jia Chen·Kuan-Hsun Chen·Jan Reineke
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Abstract

Machine-learning models are increasingly deployed on resource-constrained embedded systems with strict timing constraints. In such scenarios, the worst-case execution time (WCET) of the models is required to ensure safe operation. Specifically, decision trees are a prominent class of machine-learning models and the main building blocks of tree-based ensemble models (e.g., random forests), which are commonly employed in resource-constrained embedded systems. In this paper, we develop a systematic approach for WCET optimization of decision tree implementations. To this end, we introduce a linear surrogate model that estimates the execution time of individual paths through a decision tree based on the path's length and the number of taken branches. We provide an optimization algorithm that constructively builds a WCET-optimal implementation of a given decision tree with respect to this surrogate model. We experimentally evaluate both the surrogate model and the WCET-optimization algorithm. The evaluation shows that the optimization algorithm improves analytically determined WCET by up to $17\%$ compared to an unoptimized implementation.

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Nils Hölscher, Christian Hakert, Georg von der Brüggen, Jian-Jia Chen, Kuan-Hsun Chen, Jan Reineke. 2025-01-29. WCDT: Systematic WCET Optimization for Decision Tree Implementations. https://arxiv.org/abs/2501.17428

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