SHF: Small: Complementary Branch Predictors

为了最大化性能，现代处理器试图并行执行尽可能多的指令。这需要使用分支预测器来猜测下一个将执行哪些指令，以努力保持处理器忙碌。然而，与错误猜测的分支结果相关的惩罚对性能有很大影响。尽管分支预测在过去的二十年里得到了广泛的研究和稳步的改进，但它仍然是实现高性能和低功耗的关键。这个项目通过提供为什么分支预测器在预测动态分支的特定实例时失败的见解，在分支预测器的设计中引入了一个新的方向。研究的重点是开发互补分支预测器（CBP），它可以添加到任何传统的分支预测器（BP）。CBP智能地跟踪BP错误预测的分支。CBP通过提供BP无法预测正确路径的时间和位置的校正预测来补充BP。鉴于现代BP已经高度准确，CBP最终为最难预测的分支提供校正预测。因此，紧密耦合的BP和CBP形成一类新的快速、高精度和功率高效的分支预测器。本研究探讨了这类新的分支预测器的设计空间，以实现更高的性能，更低的功耗和更低的硬件预算。本项目的研究结果有可能显着改变未来分支预测器的设计。PI将与行业合作伙伴合作，进一步扩大更广泛的影响。这项研究将吸引研究生和本科生，并促进代表性不足的群体的参与。

To maximize performance, modern processors attempt to execute as many instructions as possible in parallel. This requires the use of branch predictors to guess which instructions will be executed next in an effort to keep the processor busy. However, the penalty associated with incorrectly guessed branch outcomes has a great impact on performance. Despite being studied extensively and improved steadily over the past two decades, branch prediction remains to be critical for achieving high performance and low power. This project introduces a new direction in the design of branch predictors by providing insights into why branch predictors fail in predicting specific instances of dynamic branches. The research focuses on developing Complementary Branch Predictors (CBP), which can be added to any conventional branch predictor (BP). CBPs intelligently track the branches that are incorrectly predicted by a BP. A CBP complements the BP by providing corrective predictions for when and where a BP fails to predict the correct path. Given that modern BPs are already highly accurate, CBPs end up providing corrective predictions for the hardest-to-predict branches. Consequently, tightly-coupled BPs and CBPs form a new class of branch predictors that are fast, highly-accurate and power-efficient. This research explores the design space of this new class of branch predictors to achieve higher performance, lower power, and lower hardware budget.The findings from this project have the potential to significantly change the design of future branch predictors. The PI will collaborate with industry partners to further increase the broader impact. The research will engage graduate and undergraduate students and promote the participation of underrepresented groups.