Merging Path and Gshare Indexing in Perceptron Branch Prediction

D. Tarjan and K. Skadron
In Univ. of Virginia Dept. of Computer Science Tech. Report CS-2004-38, Dec 2004

Abstract
We introduce the hashed perceptron predictor, which merges the concepts behind the gshare, path-based and perceptron branch predictors. This predictor can achieve superior accuracy to a path-based and a global perceptron predictor, previously the most accurate dynamic branch predictors known in the literature. We also show how such a predictor can be ahead pipelined to yield one cycle effective latency. On 11 programs from the SPECint2000 set of benchmarks, the hashed perceptron predictor improves accuracy by up to 22% over a path-based perceptron and improves IPC by up to 6.5%.

Available in pdf
This is a continuation of the work started in TR CS-2004-28
The previous perceptron papers should probably be read in the following order:
Vintan: Towards a High Performance Neural Branch Predictor(need subscription to IEEExplore)
Jiménez:Neural Methods for Dynamic Branch Prediction
Seznec:Redundant History Skewed Perceptron Predictors: pushing limits on global history branch predictors
Ipek:On Accurate and Efficient Perceptron-Based Branch Prediction
Jiménez:Fast Path-Based Neural Branch Prediction
Seznec:Revisiting the perceptron predictor

The Second Value-Prediction and Value-Based Optimization Workshop had two papers on value prediction with perceptrons.

The 1st Championship Branch Prediction produced a bunch of interesting papers using perceptrons, building upon the older work.

A couple of the predictors from the CBP got expanded into papers at ISCA 2005

Seznec:Analysis of the O-GEometric History Length branch predictor
Jiménez:Piecewise Linear Branch Prediction


Anybody have any additional suggestions? Comments?