Dibakar Gope and Mikko H. Lipasti University of Wisconsin – Madison Championship Branch Prediction 2014 Bias-Free Neural Predictor.

Slides:



Advertisements
Similar presentations
Perceptron Branch Prediction with Separated T/NT Weight Tables Guangyu Shi and Mikko Lipasti University of Wisconsin-Madison June 4, 2011.
Advertisements

André Seznec Caps Team IRISA/INRIA 1 Looking for limits in branch prediction with the GTL predictor André Seznec IRISA/INRIA/HIPEAC.
H-Pattern: A Hybrid Pattern Based Dynamic Branch Predictor with Performance Based Adaptation Samir Otiv Second Year Undergraduate Kaushik Garikipati Second.
Hardware-based Devirtualization (VPC Prediction) Hyesoon Kim, Jose A. Joao, Onur Mutlu ++, Chang Joo Lee, Yale N. Patt, Robert Cohn* ++ *
Computer Science Department University of Central Florida Adaptive Information Processing: An Effective Way to Improve Perceptron Predictors Hongliang.
André Seznec Caps Team IRISA/INRIA 1 The O-GEHL branch predictor Optimized GEometric History Length André Seznec IRISA/INRIA/HIPEAC.
Yue Hu David M. Koppelman Lu Peng A Penalty-Sensitive Branch Predictor Department of Electrical and Computer Engineering Louisiana State University.
TAGE-SC-L Branch Predictors
CPE 731 Advanced Computer Architecture ILP: Part II – Branch Prediction Dr. Gheith Abandah Adapted from the slides of Prof. David Patterson, University.
1 Lecture: Branch Prediction Topics: branch prediction, bimodal/global/local/tournament predictors, branch target buffer (Section 3.3, notes on class webpage)
IntroductionAQP FamiliesComparisonNew IdeasConclusions Adaptive Query Processing in the Looking Glass Shivnath Babu (Stanford Univ.) Pedro Bizarro (Univ.
Computer Architecture 2011 – Branch Prediction 1 Computer Architecture Advanced Branch Prediction Lihu Rappoport and Adi Yoaz.
Neural Methods for Dynamic Branch Prediction Daniel A. Jiménez Calvin Lin Dept. of Computer Science Rutgers University Univ. of Texas Austin Presented.
WCED: June 7, 2003 Matt Ramsay, Chris Feucht, & Mikko Lipasti University of Wisconsin-MadisonSlide 1 of 26 Exploring Efficient SMT Branch Predictor Design.
Perceptron-based Global Confidence Estimation for Value Prediction Master’s Thesis Michael Black June 26, 2003.
1 Improving Branch Prediction by Dynamic Dataflow-based Identification of Correlation Branches from a Larger Global History CSE 340 Project Presentation.
1 Applying Perceptrons to Speculation in Computer Architecture Michael Black Dissertation Defense April 2, 2007.
VLSI Project Neural Networks based Branch Prediction Alexander ZlotnikMarcel Apfelbaum Supervised by: Michael Behar, Spring 2005.
Computer Architecture Instruction Level Parallelism Dr. Esam Al-Qaralleh.
Dynamic Branch Prediction
Evaluation of Dynamic Branch Prediction Schemes in a MIPS Pipeline Debajit Bhattacharya Ali JavadiAbhari ELE 475 Final Project 9 th May, 2012.
Optimized Hybrid Scaled Neural Analog Predictor Daniel A. Jiménez Department of Computer Science The University of Texas at San Antonio.
1 Storage Free Confidence Estimator for the TAGE predictor André Seznec IRISA/INRIA.
Computer Architecture 2012 – advanced branch prediction 1 Computer Architecture Advanced Branch Prediction By Dan Tsafrir, 21/5/2012 Presentation based.
Analysis of Branch Predictors
Microprocessor Arch. 김인식 - 인사
1 Two research studies related to branch prediction and instruction sequencing André Seznec INRIA/IRISA.
André Seznec Caps Team IRISA/INRIA 1 Analysis of the O-GEHL branch predictor Optimized GEometric History Length André Seznec IRISA/INRIA/HIPEAC.
1 A New Case for the TAGE Predictor André Seznec INRIA/IRISA.
1 Revisiting the perceptron predictor André Seznec IRISA/ INRIA.
MadCache: A PC-aware Cache Insertion Policy Andrew Nere, Mitch Hayenga, and Mikko Lipasti PHARM Research Group University of Wisconsin – Madison June 20,
Not- Taken? Taken? The Frankenpredictor Gabriel H. Loh Georgia Tech College of Computing MICRO Dec 5, 2004.
André Seznec Caps Team IRISA/INRIA 1 A 256 Kbits L-TAGE branch predictor André Seznec IRISA/INRIA/HIPEAC.
Idealized Piecewise Linear Branch Prediction Daniel A. Jiménez Department of Computer Science Rutgers University.
1 The Inner Most Loop Iteration counter a new dimension in branch history André Seznec, Joshua San Miguel, Jorge Albericio.
Temporal Stream Branch Predictor (TS Predictor) Yongming Shen, Michael Ferdman.
Prophet/Critic Hybrid Branch Prediction B B B
André Seznec Caps Team IRISA/INRIA 1 Analysis of the O-GEHL branch predictor Optimized GEometric History Length André Seznec IRISA/INRIA/HIPEAC.
Fast Path-Based Neural Branch Prediction Daniel A. Jimenez Presented by: Ioana Burcea.
Value Prediction Kyaw Kyaw, Min Pan Final Project.
Samira Khan University of Virginia April 12, 2016
Multiperspective Perceptron Predictor Daniel A. Jiménez Department of Computer Science & Engineering Texas A&M University.
Dynamic Branch Prediction
CS203 – Advanced Computer Architecture
Computer Structure Advanced Branch Prediction
Computer Architecture Advanced Branch Prediction
Multiperspective Perceptron Predictor with TAGE
CS5100 Advanced Computer Architecture Advanced Branch Prediction
COSC3330 Computer Architecture Lecture 15. Branch Prediction
Dynamically Sizing the TAGE Branch Predictor
FA-TAGE Frequency Aware TAgged GEometric History Length Branch Predictor Boyu Zhang, Christopher Bodden, Dillon Skeehan ECE/CS 752 Advanced Computer Architecture.
Samira Khan University of Virginia Dec 4, 2017
CMSC 611: Advanced Computer Architecture
Exploring Value Prediction with the EVES predictor
Looking for limits in branch prediction with the GTL predictor
Dynamic Hardware Branch Prediction
Phase Capture and Prediction with Applications
Scaled Neural Indirect Predictor
Dynamic Branch Prediction
Lecture 10: Branch Prediction and Instruction Delivery
TAGE-SC-L Again MTAGE-SC
5th JILP Workshop on Computer Architecture Competitions
Adapted from the slides of Prof
Dynamic Hardware Prediction
rePLay: A Hardware Framework for Dynamic Optimization
Lois Orosa, Rodolfo Azevedo and Onur Mutlu
The O-GEHL branch predictor
Eshan Bhatia1, Gino Chacon1, Elvira Teran2, Paul V. Gratz1, Daniel A
Samira Khan University of Virginia Mar 6, 2019
Phase based adaptive Branch predictor: Seeing the forest for the trees
Presentation transcript:

Dibakar Gope and Mikko H. Lipasti University of Wisconsin – Madison Championship Branch Prediction 2014 Bias-Free Neural Predictor

Executive Summary Problem: Neural predictors show high accuracy 64KB restrict correlations to ~256 branches Longer history still useful (TAGE showed that) Bigger h/w increases power & training cost! Goal: + 2 Large History Limited H/W Our Solution: Filter useless context out

Key Terms Biased– Resolve as T/NT virtually every time Non-Biased– Resolve in both directions Let’s see an example … 3

Motivating Example 4 A B C D E Non-Biased Biased Left-Path Right-Path B, C & D provide No additional information

Takeaway NOT all branches provide useful context Biased branches resolve as T/NT every time –Contribute NO useful information –Existing predictors include them! Branches w/ No useful context can be omitted 5

Biased Branches 6

Bias-Free Neural Predictor 7 Conventional Weight Table ….. BFN Weight Table Recency- Stack-like GHR Positional History Folded Path History One-Dim. Weight Table Filter Biased Branches GHR: BF-GHR:

Idea 1: Filtering Biased Branches 8 Unfiltered GHR: AXYBZBCAXYBZBC Bias-Free GHR: ABBCABBC Biased: B Non-Biased: NB NBBBNBBNBNB

Idea 1: Biased Branch Detection All branches begin being considered as biased Branch Status Table (BST) –Direct-mapped –Tracks status 9

Idea 2: Filtering Recurring Instances (I) Minimize footprint of a branch in the history Assists in reaching very deep into the history 10 Unfiltered GHR: ABBCACBABBCACB Bias-Free GHR: ABCABC Non-Biased:

Idea 2: Filtering Recurring Instances (II) Recency stack tracks most recent occurrence Replace traditional GHR-like shift register 11 DQ =? DQ DQ DQ

Re-learning Correlations 12 CABBCAX X Detected Non-biased Bias-Free GHR: Unfiltered GHR: A X B C Table Index Hash Func

Idea 3: One-Dimensional Weight Table Branches Do NOT depend on relative depths in BF-GHR Use absolute depths to index 13 CABBCAX X Detected Non-biased Bias-Free GHR: Unfiltered GHR: A X B C Table Index Hash Func.

Idea 4: Positional History if (Some Condition)/ / Branch A array [ 10 ] = 1; for ( i = 0 ; i < 100 ; i ++)/ / Branch L { if ( array [ i ] == 1 ) {..... }/ / Branch X } Recency-stack-like GHR capture same history across all instances Aliasing Positional history solves that! 14 Only One instance of X correlates w/ A

Idea 5: Folded Path History A influences B differently –If path changes from M-N to X-Y Folded history solves that –Reduce aliasing on recent histories –Prevent collecting noise from distant histories 15 NAM YAX Path A-M-N Path A-X-Y B

Conventional Perceptron Component Some branches have –Strong bias towards one direction –No correlations at remote histories Problem: BF-GHR can not outweigh bias weight during training Solution: No filtering for few recent history bits 16

BFN Configuration (32KB) 17 CAB GHR: Table Index Hash Func. 2-dim weight table 1-dim weight table ZXY Loop Pred. + Is Loop? Prediction Bias-Free Unfiltered Unfiltered: recent 11 bits Bias-Free: 36 bits

Contributions of Optimizations 3 Optimizations : 1-dim weight table + phist + fhist BFN (3 Optimizations) MPKI:3.01 BFN (ghist bias-free + 3 Optimizations) MPKI:2.88 BFN (ghist bias-free + RS+ 3 Optimizations) MPKI:

Conclusion Correlate only w/ non-biased branches Recency-Stack-like policy for GHR 3 Optimizations –one-dim weight table –positional history –folded path history 47 bits to reach very deep into the history 19