Optimizing task-to-core allocation can substantially reduce power consumption in multi-core platforms without degrading user experience. However, existing approaches overlook critical factors such as parallelism, compute intensity, and heterogeneous core types. In this paper, we introduce a statistical learning approach for feature selection that identifies the most influential features-such as core type, speed, temperature, and application-level parallelism or memory intensity-for accurate environment modeling and efficient energy minimization, a critical consideration for embedded systems. Our experiments, conducted with state-of-the-art Linux governors and thermal modeling techniques, show that correlation-aware task-to-core allocation lowers energy consumption by up to 10% and reduces core temperature by up to 5C compared to random core selection. Furthermore, our compressed, bootstrapped regression model improves thermal prediction accuracy by 6% while cutting model parameters by 16%, yielding an overall mean square error reduction of 61.6% relative to existing approaches. We provided results based on superscalar Intel Core i7 12th Gen processors with 14 cores, and validated our method across a diverse set of hardware platforms and effectively balanced performance, power, and thermal demands through statistical feature evaluation.

翻译：优化任务-核心分配可在不降低用户体验的前提下显著降低多核平台的功耗。然而，现有方法忽视了并行度、计算强度及异构核心类型等关键因素。本文提出一种用于特征选择的统计学习方法，该方法通过识别最具影响力的特征——如核心类型、速度、温度以及应用级并行度或内存强度——以实现精确的环境建模与高效的能量最小化，这对嵌入式系统至关重要。我们采用先进的Linux调控器与热建模技术进行实验，结果表明：相较于随机核心选择，基于相关性感知的任务-核心分配可降低高达10%的能耗，并使核心温度降低多达5°C。此外，我们提出的压缩式自助回归模型将热预测精度提升6%，同时减少16%的模型参数，相较于现有方法，整体均方误差降低了61.6%。我们在配备14个核心的超标量Intel Core i7第12代处理器上提供了实验结果，并通过统计特征评估，在多样化硬件平台上验证了本方法能有效平衡性能、功耗与热管理需求。