Merging path and gshare indexing in perceptron branch prediction

Authors:
David Tarjan;Kevin Skadron
Affiliations:
University of Virginia, Charlottesville, VA;University of Virginia, Charlottesville, VA
Venue:
ACM Transactions on Architecture and Code Optimization (TACO)
Year:
2005

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Abstract

We introduce the hashed perceptron predictor, which merges the concepts behind the gshare, path-based and perceptron branch predictors. This predictor can achieve superior accuracy to a path-based and a global perceptron predictor, previously the most accurate dynamic branch predictors known in the literature. We also show how such a predictor can be ahead pipelined to yield one cycle effective latency. On the SPECint2000 set of benchmarks, the hashed perceptron predictor improves accuracy by up to 15.6% over a MAC-RHSP and 27.2% over a path-based neural predictor.