WCDT: Systematic WCET Optimization for Decision Tree Implementations

• URL: http://arxiv.org/abs/2501.17428v1
• Date: Wed, 29 Jan 2025 06:01:39 GMT
• Title: WCDT: Systematic WCET Optimization for Decision Tree Implementations
• Authors: Nils Hölscher, Christian Hakert, Georg von der Brüggen, Jian-Jia Chen, Kuan-Hsun Chen, Jan Reineke,
• Abstract summary: Worst-case execution time (WCET) of machine-learning models is required to ensure safe operation. We develop a systematic approach for WCET optimization of decision tree implementations. We experimentally evaluate both the surrogate model and the WCET-optimization algorithm.
• Score: 4.95559363788634
• License:
• 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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