Machine Learning for Energy-Performance-aware Scheduling
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.
69804c7d6676f933227066c6
Zheyuan Hu
claimed_verified
2026-02-02T16:52:09.611Z
653053c6657ae56cdb5c490b
https://cdn-avatars.huggingface.co/v1/production/uploads/653053c6657ae56cdb5c490b/Zl0bZZ7-tD9AR6muE8esU.png
Peter Hu
Peter2023HuggingFace
user
Peter2023HuggingFace
69804c7d6676f933227066c7
Yifei Shi
Machine Learning for Energy-Performance-aware Scheduling
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.
A Bayesian Optimization approach using Gaussian Processes automates scheduling configuration optimization on heterogeneous multi-core systems while approximating the Pareto Frontier for energy-time trade-offs.
Bayesian Optimization Gaussian Processes multi-objective optimization Pareto Frontier sensitivity analysis fANOVA covariance kernels Matérn RBF