Enabling Ahead Prediction with Practical Energy Constraints

Accurate branch predictors require multiple cycles to produce a prediction, and that latency hurts processor performance. "Ahead prediction" solves the performance problem by starting the prediction early. Unfortunately, this means making the prediction without the directions of the N branches between when the prediction starts and when the relevant branch's prediction is needed. The energy required to consider all 2 𝑛 possible cases increases by 14.6x, making ahead prediction not viable. This paper shows that most of the intermediate branch directions never materialize, reducing the number of observed missing history patterns significantly (usually, only one or two). We modified the TAGE predictor to eliminate those branches from having to be considered. The result, our ahead predictor can produce a performance benefit of 4.4%, while causing an increase in energy consumption of only 1.5x, far less than the 14.6x that was thought to be necessary, and very much viable.