1. Scaled Neural Indirect Predictor*
07/16/96
Scaled Neural Indirect Predictor
Daniel A. Jiménez
Department of Computer Science
The University of Texas at San Antonio *

2. Basic Idea Predict selected bits of target address
Attempt to match these bits to known targets
Target with minimum Hamming distance is prediction

3. Basic Idea cont.

4. Components Tagless set-associative memory like a BTB
Indexed by bits of branch address
Filled with branch targets with LRU replacement
Predictors
Each predicts one bit of target
SNAP predictor provides good accuracy
Use conditional branch path/pattern history

5. Tricks Use some of the same tricks used for OH-SNAP
Training coefficient vectors
Adaptively train threshold
Separate bias weights from correlating weights
All predictors use the same tables of weights
Only predict certain lower order bits
In our case, matching the following mask:

6. Short Presentation
The abstract idea of Hamming-distance-based target prediction is very simple
The intelligence in this indirect predictor is in the one-bit predictors
OH-SNAP in our case

7. The End