基于统计学习的嵌入式多核任务分配，显著降低功耗与温度。
Feature-Aware Task-to-Core Allocation in Embedded Multi-core Platforms via Statistical Learning
- 通过特征选择识别核心类型、并行度等关键影响因素。
- 相比随机分配，能耗降低10%，核心温度下降5℃。
- 模型参数减少16%且热预测精度提升6%，适合资源受限场景。
优化任务到核心的分配可显著降低多核平台的功耗而不影响用户体验。然而，现有方法忽略了并行度、计算强度和异构核心类型等关键因素。本文提出一种基于统计学习的特征选择方法，识别核心类型、速度、温度以及应用级并行度或内存强度等最具影响力的特征，以实现精准环境建模与高效能效优化，这对嵌入式系统至关重要。实验基于第12代Intel Core i7超标量处理器（14核）及先进Linux调度器与热建模技术，结果显示：相关性感知的任务分配使能耗降低最多达10%，核心温度降低最多5℃。此外，压缩的自助回归模型在模型参数减少16%的同时，热预测精度提升6%，相较现有方法整体均方误差降低61.6%。验证覆盖多种硬件平台，有效平衡了性能、功耗与热管理需求。
原文摘要 · Abstract (English)
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.
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