Daniel A. Jimenez
djimenez@cs.utexas.edu
http://www.cs.utexas.edu/users/djimenez


Title: Dynamic Branch Prediction with Perceptrons

Abstract:

Modern pipelined microprocessors consult branch predictors to speculatively
fetch and execute instructions beyond conditional branches.  We present a
new method for branch prediction.  The key idea is to replace the commonly
used two-bit counter with a perceptron, one of the simplest possible neural
networks.  Perceptrons provide better predictive capabilities than counters
and allow our predictor to consider longer branch histories.  The hardware
resources needed for our method scale linearly with the history length, in
contrast with other purely dynamic schemes that require exponential memory.
Using a hierarchical organization, this complex multi-cycle predictor can
be used as a component of a fast delay sensitive predictor.