Signals Approaches to Computer Architecture Prediction Mechanisms

这项研究致力于开发一种新的、对称的计算机体系结构预测框架，该框架受数据分析的信号方法的推动。这项研究使用了两个维度的预测风格：周期性与计算性，局部历史与全球历史。这是建立在离散信号分析原理之上的。在预测机制方面已经做了大量的工作，特别是关于分支行为的预测机制。这项新工作的最初重点将是价值预测，因为这是人们最不了解的领域。然而，框架和方法适用于分支预测、相关性预测等。本研究将使用两种技术来研究新的预测方案：局部和全局方案之间的类比，以及在计算预测器的情况下的数据分析技术。由于管道延迟，全局预测器可能会失去准确性。本研究项目将研究避免全局预测器延迟惩罚的方法。详细的微体系结构模拟涉及大量的权衡。对这一背景下的预测因素进行详细研究是本项目中包含的一项重要研究。在以具有成本效益和实用的方式组合预测值方面，还有大量的工作要做。

This research is focused on developing a new, symmetric framework for computer architecture prediction motivated by the signals approach to data analysis. This research uses styles of prediction in two dimensions: periodic vs. computational and local history vs. global history. This is built on top of the principles of discrete signal analysis. There has been a significant amount of work done on prediction mechanisms, especially for branch behavior. The initial focus of this new work will be on value prediction because it is the least well understood area. However, the framework and methodology are applicable to branch prediction, dependence prediction, etc. This research will investigate new prediction schemes using two techniques: analogy between local and global schemes, and data analysis techniques in the case of a computational predictor. Global predictors can lose accuracy due to pipeline delays. This research project will investigate methods for avoiding the delay penalty for global predictors. The detailed microarchitectural simulation involves a high number of tradeoffs. A detailed study of predictors in that context is a significant study that is included in this project. There is also considerable work to be performed on combining predictors in cost effective and practical ways.