An Optimized Scaled Neural Branch Predictor Conference Paper

abstract

• 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. We present an optimized version of the SNAP predictor, hybridized with two simple two-level adaptive predictors. The resulting optimized predictor, OH-SNAP, delivers very high accuracy compared with other state-of-the-art predictors. 2011 IEEE.

name of conference

• 2011 IEEE 29th International Conference on Computer Design (ICCD)

authors

• Jimenez, Daniel

published proceedings

• 2011 IEEE 29th International Conference on Computer Design (ICCD)

author list (cited authors)

• Jimnez, D. A.

citation count

• 18

complete list of authors

• Jiménez, Daniel A

publication date

• January 2011

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE International Conference on Computer Design - VLSI in Computers and Processors  Journal

Digital Object Identifier (DOI)

• 10.1109/iccd.2011.6081385

International Standard Book Number (ISBN) 13

• 9781457719523

start page

• 113

end page

• 118

URL

• http://dx.doi.org/10.1109/iccd.2011.6081385