FoMR: Adaptive Branch Prediction

微处理器通过以流水线方式执行指令来运行程序。第一步决定下一步处理哪条指令。当到达决策分支指令时，处理器直到几个步骤后才能获得下一条指令。为了避免延迟，处理器预测下一条指令将来自哪里。这个项目研究了通过使预测器适应程序执行期间不断变化的条件来提高这一预测和相关预测的准确性的方法。该项目的成功成果将提高移动电话、台式和笔记本电脑以及数据中心的效率。该项目将从代表性不足的群体中招收研究生，并为课堂教学做出贡献。该项目专注于由机器学习驱动的微体系结构预测器。这些预测器使用了大量的输入特征。目前，功能是在设计时固定的，以涵盖广泛的可能程序行为。该项目探讨了如何针对当前工作负载自适应地学习功能。这种适应将提高准确性，同时通过使用更少的功能来降低硬件开销。这一方法适用于几种预测器，包括条件和间接分支预测、高速缓存重用预测、预取过滤、分支目标缓冲区中的替换、指令高速缓存和转换后备缓冲区。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Microprocessors run programs by executing instructions in an assembly-line fashion. The first step decides which instruction to work on next. When a decision-making branch instruction is reached, the processor can't get the next instruction until the decision is made several steps later. To avoid delays, processors predict where the next instruction will come from. This project investigates ways of improving the accuracy of this and related predictions by adapting the predictor to changing conditions during program execution. Successful outcomes of this project will improve efficiency for mobile phones, desktop and laptop computers, and datacenters. The project will recruit graduate students from under-represented groups and contribute to classroom teaching.The project focuses on microarchitectural predictors driven by machine learning. These predictors use a large number of input features. Currently, features are fixed at design time to cover a wide range of possible program behaviors. The project explores how to adaptively learn features for the current workload. This adaptation will improve accuracy while reducing hardware overhead by using fewer features. This approach applies to several kinds of predictors including conditional and indirect branch prediction, cache reuse prediction, prefetch filtering, replacement in the branch target buffer, instruction cache, and translation look-aside buffers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.