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Scaled Neural Indirect Predictor

Explore the innovation of the SNAP predictor in predicting branch target addresses with minimal Hamming distance, incorporating set-associative memory and training coefficient vectors for improved accuracy.

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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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