Spotlight - a low complexity highly accurate profile-based branch predictor

In an effort to achieve the high prediction accuracy needed to attain high instruction throughputs, branch predictors proposed in the literature and used in real systems have become increasingly more complex and larger over time. This is not consistent with the anticipated trend of simpler and more numerous cores in future multi-core processors. We introduce the Spotlight branch predictor, a novel profile-based predictor which is able to achieve high prediction accuracy despite its simple design. Spotlight achieves high accuracy because complex decisions in the prediction process are made during an OS managed, one-time profile run instead of using complex hardware. We show that Spotlight achieves higher accuracy than Gshare as well as highly accurate and implementable predictors such as YAGS and the hybrid bimodal-Gshare predictor. It achieves an average reduction in misprediction rate of 20% over Gshare, 11% over elastic history buffer, 14% over Yags and 10% over hybrid for a hardware budget of 8 kB. Spotlight is also compared to two difficult to implement neural predictors, the path-based neural and the hashed perceptron. It outperforms the path-based neural predictor at all sizes and the hashed perceptron at smaller hardware budgets. These results demonstrate that a simple profile-based predictor can achieve many of the benefits of more complex predictors. We also show that a single cycle latency implementation of Spotlight can be achieved without sacrificing accuracy by using an upstream replacement scheme.