Dynamic feature selection for hardware prediction
Most hardware predictors are table based (e.g. two-level branch predictors) and have exponential size growth in the number of input bits or features (e.g. previous branch outcomes). This growth severely limits the amount of predictive information that such predictors can use. To avoid exponential growth we introduce the idea of "dynamic feature selection" for building hardware predictors that can use a large amount of predictive information. Based on this idea, we design the dynamic decision tree (DDT) predictor, which exhibits only linear size growth in the number of features. Our initial evaluation, in branch prediction, shows that the general-purpose DDT, using only branch-history features, is comparable on average to conventional branch predictors, opening the door to practically using large numbers of additional features. © 2005 Elsevier B.V. All rights reserved.
Record created on 2009-04-06, modified on 2016-08-08