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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.17428 |
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| _version_ | 1866909469276897280 |
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| author | Hölscher, Nils Hakert, Christian von der Brüggen, Georg Chen, Jian-Jia Chen, Kuan-Hsun Reineke, Jan |
| author_facet | Hölscher, Nils Hakert, Christian von der Brüggen, Georg Chen, Jian-Jia Chen, Kuan-Hsun Reineke, Jan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17428 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WCDT: Systematic WCET Optimization for Decision Tree Implementations Hölscher, Nils Hakert, Christian von der Brüggen, Georg Chen, Jian-Jia Chen, Kuan-Hsun Reineke, Jan Machine Learning Performance 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. |
| title | WCDT: Systematic WCET Optimization for Decision Tree Implementations |
| topic | Machine Learning Performance |
| url | https://arxiv.org/abs/2501.17428 |