An Optimized Scaled Neural Branch Predictor

Conditional branch prediction remains one of the most important enabling technologies for high-performance microprocessors. A small improvement in accuracy can result in a large improvement in performance as well as a significant reduction in energy wasted on wrong-path instructions. Neural-based branch predictors have been among the most accurate in the literature. The recently proposed scaled neural analog predictor, or SNAP, builds on piecewise-linear branch prediction and relies on a mixed analog/digital implementation to mitigate latency as well as power requirements over previous neural predictors. The authors present an optimized version of the SNAP predictor, hybridized with two simple two-level adaptive predictors.

Provided by: University of Texas Topic: Data Centers Date Added: Oct 2011 Format: PDF

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