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Scaled Neural Indirect Predictor

Scaled Neural Indirect Predictor. Daniel A. Jiménez Department of Computer Science The University of Texas at San Antonio. Basic Idea. Predict selected bits of target address Attempt to match these bits to known targets Target with minimum Hamming distance is prediction. Basic Idea cont.

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Scaled Neural Indirect Predictor

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  1. 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: • ...00000010110101111111111010

  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

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