Dynamic Feature Selection for Hardware Prediction

DMCA

Dynamic Feature Selection for Hardware Prediction (2000)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Alan Fern , Robert Givan , Babak Falsafi , T. N. Vijaykumar

Venue:	PURDUE UNIVERSITY
Citations:	15 - 0 self

BibTeX

@TECHREPORT{Fern00dynamicfeature,
    author = {Alan Fern and Robert Givan and Babak Falsafi and T. N. Vijaykumar},
    title = {Dynamic Feature Selection for Hardware Prediction},
    institution = {PURDUE UNIVERSITY},
    year = {2000}
}

OpenURL

Abstract

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.

Keyphrases

dynamic feature selection hardware prediction predictive information hardware predictor exponential growth additional feature input bit linear size growth exponential size growth two-level branch predictor conventional branch predictor previous branch outcome initial evaluation branch-history feature large number general-purpose ddt branch prediction large amount dynamic decision tree

DMCA