Power management is one of the significant challenges to be addressed in multi-/many-core microprocessors. Furthermore, the multi-core microprocessors experience unforeseen scenarios such as performance degradation over time, manufacturing defects, power, and thermal impacts with time. Traditional power management techniques, though efficient, is not designed to handle such unseen scenarios. Furthermore, the variation in performance requirements is one of the challenges faced in the era of machine learning. We propose a self-aware power management scheme for multi-core microprocessors in this work to address the above-mentioned issues. We perform application-level power management in this work to overcome the overheads imposed by core-level power management and system-level power management inefficiency. The power management unit employs a linear predictor for workload prediction to perform DVFS. On top of the power manager, the self-aware controller is hierarchically placed to monitor the system components’ health and adapt the power manager's decision to meet the performance requirements and handle changes in system components’ health. We evaluate the proposed self-aware power manager under externally provided high performance goals, and resource contention. A power saving of up to 16% compared to existing power management techniques, and 2.4$\times$ speedup with 25% additional power to satisfy high performance compared to power management without self-awareness for a microprocessor with up to 32-cores is achieved.

电源管理是多核/多核微处理器要解决的重大挑战之一。此外，多核微处理器会遇到无法预料的情况，例如随着时间的推移性能下降，制造缺陷，功耗以及随时间变化的热影响。传统的电源管理技术虽然有效，但并未设计用于处理这种看不见的情况。此外，性能要求的变化是机器学习时代面临的挑战之一。我们在这项工作中提出了一种针对多核微处理器的自我感知电源管理方案，以解决上述问题。我们在这项工作中执行应用程序级电源管理，以克服核心级电源管理和系统级电源管理效率低下带来的开销。电源管理单元将线性预测器用于工作量预测以执行DVFS。在电源管理器的顶部，将自我感知的控制器分层放置，以监视系统组件的运行状况，并使电源管理器的决定适应性能要求并处理系统组件的运行状况变化。我们在外部提供的高性能目标和资源争用情况下评估拟议的自我意识电源管理器。与现有的电源管理技术相比，可节省多达16％的电源，而2.4 我们在外部提供的高性能目标和资源争用情况下评估拟议的自我意识电源管理器。与现有的电源管理技术相比，可节省多达16％的电源，而2.4 我们在外部提供的高性能目标和资源争用情况下评估拟议的自我意识电源管理器。与现有的电源管理技术相比，可节省多达16％的电源，而2.4$\times$ 与不带自我意识的最多32核微处理器的电源管理相比，可实现25％的额外电源加速性能，以满足高性能。