用贝叶斯优化自动寻找多核系统中能效与延迟的最优调度方案。
Machine Learning for Energy-Performance-aware Scheduling
- 基于高斯过程的贝叶斯优化框架，自动搜索复杂硬件配置。
- 同时优化能效与延迟，逼近帕累托前沿，实现双目标平衡。
- 通过敏感性分析揭示关键硬件参数，提升模型可解释性。
在后登纳德时代，优化嵌入式系统需权衡能效与延迟之间的复杂关系。传统启发式调优在高维、非平滑的优化空间中效率低下。本文提出一种基于高斯过程的贝叶斯优化框架，用于在异构多核架构上自动搜索最优调度配置。通过近似能量与时间之间的帕累托前沿，显式处理多目标优化问题。此外，结合敏感性分析（fANOVA）并对比不同核函数（如Matérn与RBF），赋予黑箱模型物理可解释性，揭示影响系统性能的关键硬件参数。
原文摘要 · Abstract (English)
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.
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