Optimized Hybrid Scaled Neural Analog Predictor

1. Optimized Hybrid Scaled Neural Analog Predictor Daniel A. Jiménez Department of Computer Science The University of Texas at San Antonio

2. Branch Prediction with Perceptrons

3. Branch Prediction with Perceptrons cont.

4. 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

5. Weight Scaling • Scaling weights by coefficients Different history positions have different importance!

6. 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 • θ – a threshold to decide when to train

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

8. The Algorithm: Training • If the prediction is wrong or |output| ≤ θ then • For the ithcorrelating weight used to predict this branch: • Increment it if the branch outcome = outcome of ithin history • Decrement it otherwise • Increment the bias weight if branch is taken • Decrement otherwise

9. SNP/SNAP Datapath

10. 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

11. 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 • >99% hit rate

12. 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 θs indexed by branch PC • Each trained adaptively[Seznec 2005] • Ragged array • Not all rows of the matrix are the same size

13. Benefit of Tricks • Graph shows effect of one trick in isolation • Training coefficients yields most benefit

14. 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

15. The End