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Neural methods for dynamic branch prediction
2002
ACM Transactions on Computer Systems
This article presents a new and highly accurate method for branch prediction. The key idea is to use one of the simplest possible neural methods, the perceptron, as an alternative to the commonly used two-bit counters. The source of our predictor's accuracy is its ability to use long history lengths, because the hardware resources for our method scale linearly, rather than exponentially, with the history length. We describe two versions of perceptron predictors, and we evaluate these predictors
doi:10.1145/571637.571639
fatcat:qfztavkunfbrbkjwxvjvhng3je