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.

翻译：在后登纳德时代，优化嵌入式系统需要在能效与延迟之间进行复杂的权衡。传统启发式调调方法在这种高维非光滑的优化空间中往往效率低下。本研究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。我们通过近似能量与时间之间的帕累托前沿，显式处理了问题的多目标特性。此外，通过引入敏感性分析（fANOVA）并比较不同协方差核函数（如Matérn核与径向基函数核），我们为黑盒模型提供了物理解释性，揭示了驱动系统性能的主导硬件参数。