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The Fr ankenpredictor Gabriel H. Loh Georgia Tech College of Computing CBP-1 @ MICRO Dec 5, 2004

Taken?. Not-Taken?. The Fr ankenpredictor Gabriel H. Loh Georgia Tech College of Computing CBP-1 @ MICRO Dec 5, 2004. Design Objectives. Capacity SERV has large branch footprint Long history length correlation INT and MM benefit from this 8KB makes interference a big issue

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The Fr ankenpredictor Gabriel H. Loh Georgia Tech College of Computing CBP-1 @ MICRO Dec 5, 2004

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  1. Taken? Not-Taken? The FrankenpredictorGabriel H. LohGeorgia TechCollege of ComputingCBP-1 @ MICRODec 5, 2004

  2. Design Objectives • Capacity • SERV has large branch footprint • Long history length correlation • INT and MM benefit from this • 8KB makes interference a big issue • … and don’t mess up FP

  3. Overview Perceptron Table Global BHR + Path-Neural: Long History Fusion Gskew-Agree: Capacity Ideas from: static branch prediction, gskewed, agree, perceptron, path-based neural, 2bcgskew, fusion, MAC-RHSP, machine learning Misc Other Ideas

  4. Gskew-Agree Lookup Global BHR h0 h1 h2 PC BIM G0 G1 0 1 Target 1 >? MAJ BTFNT =? =? =? =? xg1 xg2 xgM xg0

  5. Gskew-Agree Update • Shared Hysteresis • Every two counters share one hyst. bit • Partial Update • Modified from 2bcgskew (no meta rules) • On mispred, update all three tables • If correct: • If all agree, do nothing • Else update the two correct tables 0 1 1

  6. Old History Gskew-Agree x11 x12 x13 ... x59 xg0 xg1 xg2 xgM X X X X X X X X B3 B2 ... 42 rows, 7-bit weights Path-Based Perceptron Bias, Pseudotag, Recent History x0 xp0 xp1 x1 x2 x3 … x10 + X X X X X X X B1 ... 84 rows, 8-bit weights 168 rows, 8-bit weights

  7. Redundant Indexing Bias, Pseudotag, Recent History Old History Gskew-Agree … … X X X X + X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X ... ...

  8. Actual Value Actual Value Actual Value Stored Value Stored Value Stored Value Non-Linear Learning Curves B2 (older history) B1, B3 • Slow start: • avoids transient/coincidental correlation • Steep End: • quick unlearning

  9. Global BHR G0 hash =? xg1 hash sign =? << 1 Synergistic Reinforcement BIM hash PC BTFNT =? xg0 hash sign =? To Summation << 1

  10. Notes on Initialization • All neural weights initialized to zero except those corresponding to xgM • The perceptron will use the gskew-agree prediction until other correlations are established • All PHT banks initialized to “Weakly Agree” • gskew-agree provides a BTFNT prediction at start of program

  11. Unconditional Branches • Always predict taken, of course • No update to PHTs, neural weights, path • Update global BHR: PC Call: xor 00000000 Return: xor 11111111 Other: xor 01010101

  12. “Path” History Update Outcome Global BHR … Branch Address PC Path1 Path2 Shift registers Path3 Pa4 Pa5 Pa6 Pa7 Pa8 Pa9 Pa10 …

  13. Redundant Index: 6 most recent BHR bits + + + Index Generation B1 B2 Path History Primary Index: PC 3 LSB … …

  14. Implementation Issues • Frankenpredictor optimized for state • Neural table sizes not power-of-two • Redundant indexing biggest challenge • Many hash functions • Many ports • Huge adder tree

  15. Summary • Gskew for capacity, Neural for long-history • Neural also for fusion • Gskew-Agree • Skewing for interference-avoidance • Agree-prediction for interference-tolerance • Contact: • loh <at> cc.gatech.edu • http://www.cc.gatech.edu/~loh

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