PANDA：通过融合解析方案与机器学习方案实现架构级功耗评估
PANDA: Architecture-Level Power Evaluation by Unifying Analytical and Machine Learning Solutions
AI总结：
针对现有架构级功耗模型准确性不足或依赖大量训练设计的问题，本研究提出融合解析与机器学习方案的PANDA模型，在少训练样本下仍具高准确性，还可预测多指标及新工艺节点功耗，并能完成功耗约束下的设计配置筛选。
摘要：
功耗效率是现代微处理器设计中的核心设计目标。为评估架构级设计决策的影响，业界需要一种兼具准确性与高效性的架构级功耗模型。然而，McPAT、Wattch等被广泛采用的与数据无关的解析功耗模型，因准确性不可靠而饱受诟病。尽管已有部分机器学习（ML）方法被提出用于架构级功耗建模，但这类方法依赖充足的已知设计进行训练，当可用设计数量有限时表现不佳——而这正是现实场景中的典型情况。\n在本研究中，我们推导出一个统一现有架构级功耗模型的通用公式。基于该公式，我们提出了PANDA，这是一种创新性的架构级解决方案，融合了解析功耗模型与ML功耗模型的优势。即便训练用设计数量极少（这是实践中的常见挑战），它也能在未知的新设计上实现前所未有的高准确性。除了作为出色的功耗模型，它还能准确预测面积、性能和能耗。PANDA还进一步支持对未知新工艺节点的功耗预测。在我们的实验中，除了验证PANDA的优越性能和广泛功能外，我们还提出了一个应用场景：在给定功耗约束的情况下，PANDA被证明能够识别出高性能的设计配置。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
