Power efficiency is a critical design objective in modern microprocessor design. To evaluate the impact of architectural-level design decisions, an accurate yet efficient architecture-level power model is desired. However, widely adopted data-independent analytical power models like McPAT and Wattch have been criticized for their unreliable accuracy. While some machine learning (ML) methods have been proposed for architecture-level power modeling, they rely on sufficient known designs for training and perform poorly when the number of available designs is limited, which is typically the case in realistic scenarios. In this work, we derive a general formulation that unifies existing architecture-level power models. Based on the formulation, we propose PANDA, an innovative architecture-level solution that combines the advantages of analytical and ML power models. It achieves unprecedented high accuracy on unknown new designs even when there are very limited designs for training, which is a common challenge in practice. Besides being an excellent power model, it can predict area, performance, and energy accurately. PANDA further supports power prediction for unknown new technology nodes. In our experiments, besides validating the superior performance and the wide range of functionalities of PANDA, we also propose an application scenario, where PANDA proves to identify high-performance design configurations given a power constraint.

翻译：功耗效率是现代微处理器设计中的关键设计目标。为评估架构级设计决策的影响，需要一种既准确又高效的架构级功耗模型。然而，McPAT和Wattch等广泛采用的数据无关分析型功耗模型因其不可靠的准确性而受到批评。尽管已有一些机器学习（ML）方法被提出用于架构级功耗建模，但这些方法依赖足够的已知设计进行训练，而当可用设计数量有限时（这在现实场景中通常是常见情况），其性能表现不佳。在本工作中，我们推导出一种统一现有架构级功耗模型的通用公式。基于该公式，我们提出PANDA——一种创新性的架构级解决方案，它结合了分析型和ML功耗模型的优势。即使在训练设计非常有限的常见挑战下，PANDA也能在未知新设计上实现前所未有的高精度。除了作为优秀的功耗模型外，它还能准确预测面积、性能和能耗。PANDA进一步支持未知新工艺节点的功耗预测。在我们的实验中，除了验证PANDA的卓越性能和广泛功能外，我们还提出一个应用场景——在该场景中，PANDA能够有效识别给定功耗约束下的高性能设计配置。