Signals Approaches to Computer Architecture Prediction Mechanisms

这项研究的重点是开发一个新的，对称的框架，计算机体系结构预测的信号方法的数据分析的动机。本研究在两个维度上使用预测风格：周期性与计算性以及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，特别是对于分支行为。这项新工作的最初重点将是价值预测，因为这是最不容易理解的领域。然而，该框架和方法适用于分支预测，依赖预测等。本研究将探讨新的预测方案，使用两种技术：局部和全局方案之间的类比，和数据分析技术的情况下，计算预测。全局预测器可能由于管道延迟而失去准确性。本研究计划将探讨避免全球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。在这种情况下，对预测因素的详细研究是本项目中的一项重要研究。在以具有成本效益和实用的方式结合预测因素方面，还有大量工作要做。

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.