Power-aware branch predictor update

摘要

Designers have invested much effort in developing accurate branch predictors. To maintain accuracy, current processors update the predictor regularly and frequently. Although this aggressive approach helps to achieve high accuracy, for a large number of branches, quite often, updating the branch predictor unit is unnecessary as there is already enough information available to the predictor to predict the branch outcome accurately. Therefore, the current approach appears to be inefficient since it results in unnecessary energy consumption. The author introduces the power-aware branch predictor update (PABU). PABU uses a simple power efficient structure to identify well behaved accurately predicted branch instructions. Once such branches are identified, the predictor is no longer accessed to update the associated data. The key to the success of the proposed technique is a power efficient method that can effectively identify such branches. The author exploits branch instruction behaviour to identify such branch instructions. He shows that it is possible to reduce the number of predictor updates considerably without losing performance. The technique is evaluated by studying energy and performance tradeoffs for SPEC2000 benchmarks. It is shown that the technique can reduce branch prediction energy consumption considerably for both floating point and integer benchmarks. This comes with a negligible impact on performance.