Machine Learning for Energy-Performance-aware Scheduling
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910006278881280 |
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| author | Hu, Zheyuan Shi, Yifei |
| author_facet | Hu, Zheyuan Shi, Yifei |
| contents | In the post-Dennard era, optimizing embedded systems requires navigating complex trade-offs between energy efficiency and latency. Traditional heuristic tuning is often inefficient in such high-dimensional, non-smooth landscapes. In this work, we propose a Bayesian Optimization framework using Gaussian Processes to automate the search for optimal scheduling configurations on heterogeneous multi-core architectures. We explicitly address the multi-objective nature of the problem by approximating the Pareto Frontier between energy and time. Furthermore, by incorporating Sensitivity Analysis (fANOVA) and comparing different covariance kernels (e.g., Matérn vs. RBF), we provide physical interpretability to the black-box model, revealing the dominant hardware parameters driving system performance. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_23134 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine Learning for Energy-Performance-aware Scheduling Hu, Zheyuan Shi, Yifei Hardware Architecture Artificial Intelligence Machine Learning C.4; I.2.6; I.2.8 In the post-Dennard era, optimizing embedded systems requires navigating complex trade-offs between energy efficiency and latency. Traditional heuristic tuning is often inefficient in such high-dimensional, non-smooth landscapes. In this work, we propose a Bayesian Optimization framework using Gaussian Processes to automate the search for optimal scheduling configurations on heterogeneous multi-core architectures. We explicitly address the multi-objective nature of the problem by approximating the Pareto Frontier between energy and time. Furthermore, by incorporating Sensitivity Analysis (fANOVA) and comparing different covariance kernels (e.g., Matérn vs. RBF), we provide physical interpretability to the black-box model, revealing the dominant hardware parameters driving system performance. |
| title | Machine Learning for Energy-Performance-aware Scheduling |
| topic | Hardware Architecture Artificial Intelligence Machine Learning C.4; I.2.6; I.2.8 |
| url | https://arxiv.org/abs/2601.23134 |