Optimized Hybrid Scaled Neural Analog Predictor

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Optimized Hybrid Scaled Neural Analog Predictor
Daniel A. Jiménez Department of Computer
Science The University of Texas at San Antonio
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Branch Prediction with Perceptrons
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Branch Prediction with Perceptrons cont.
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SNP/SNAP St. Amant et al. 2008

• A version of piecewise linear neural prediction
  Jiménez 2005
• Based on perceptron prediction
• SNAP is a mixed digital/analog version of SNP
• Uses analog circuit for costly dot-product
  operation
• Enables interesting tricks e.g. scaling

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Weight Scaling

• Scaling weights by coefficients

Different history positions have different
importance!
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The Algorithm Parameters and Variables

• C array of scaling coefficients
• h the global history length
• H a global history shift register
• A a global array of previous branch addresses
• W an n (GHL 1) array of small integers
• ? a threshold to decide when to train

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The Algorithm Making a Prediction
Weights are selected based on the current branch
and the ith most recent branch
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The Algorithm Training

• If the prediction is wrong or output ? then
• For the ith correlating weight used to predict
  this branch
• Increment it if the branch outcome outcome of
  ith in history
• Decrement it otherwise
• Increment the bias weight if branch is taken
• Decrement otherwise

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SNP/SNAP Datapath
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Tricks

• Use alloyed Skadron 2000 global and per-branch
  history
• Separate table of local perceptrons
• Output from this stage multiplied by empircally
  determined coefficient
• Training coefficients vector(s)
• Multiple vectors initialized to f(i) 1 / (A B
  i)
• Minimum coefficient value determined empircally
• Indexed by branch PC
• Each vector trained with perceptron-like learning
  on-line

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Tricks(2)

• Branch cache
• Highly associative cache with entries for branch
  information
• Each entry contains
• A partial tag for this branch PC
• The bias weight for this branch
• An ever taken bit
• A never taken bit
• The ever/never bits avoid needless use of
  weight resources
• The bias weight is protected from destructive
  interference
• LRU replacement
• gt99 hit rate

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Tricks(3)

• Hybrid predictor
• When perceptron output is below some threshold
• If a 2-bit counter gshare predictor has high
  confidence, use it
• Else use a 1-bit counter PAs predictor
• Multiple ?s indexed by branch PC
• Each trained adaptively Seznec 2005
• Ragged array
• Not all rows of the matrix are the same size

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Benefit of Tricks

• Graph shows effect of one trick in isolation
• Training coefficients yields most benefit

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References

• Jiménez Lin, HPCA 2001 (perceptron predictor)
• Jiménez Lin, TOCS 2002 (global/local
  perceptron)
• Jiménez ISCA 2005 (piecewise linear branch
  predictor)
• Skadron, Martonosi Clark, PACT 2000 (alloyed
  history)
• Seznec 2005 (adaptively trained threshold)
• St. Amant, Jiménez Burger, MICRO 2008
  (SNP/SNAP)
• McFarling 1993, gshare
• Yeh Patt 1991, PAs

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The End