gem5 / public / gem5 / b68735f9fad5879e606849e78e1a4ad3f77f1463 / . / src / cpu / pred / BranchPredictor.py

# Copyright (c) 2012 Mark D. Hill and David A. Wood | |

# Copyright (c) 2015 The University of Wisconsin | |

# All rights reserved. | |

# | |

# Redistribution and use in source and binary forms, with or without | |

# modification, are permitted provided that the following conditions are | |

# met: redistributions of source code must retain the above copyright | |

# notice, this list of conditions and the following disclaimer; | |

# redistributions in binary form must reproduce the above copyright | |

# notice, this list of conditions and the following disclaimer in the | |

# documentation and/or other materials provided with the distribution; | |

# neither the name of the copyright holders nor the names of its | |

# contributors may be used to endorse or promote products derived from | |

# this software without specific prior written permission. | |

# | |

# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS | |

# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT | |

# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR | |

# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT | |

# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, | |

# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT | |

# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, | |

# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY | |

# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT | |

# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | |

# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | |

# | |

# Authors: Nilay Vaish and Dibakar Gope | |

from m5.SimObject import SimObject | |

from m5.params import * | |

from m5.proxy import * | |

class IndirectPredictor(SimObject): | |

type = 'IndirectPredictor' | |

cxx_class = 'IndirectPredictor' | |

cxx_header = "cpu/pred/indirect.hh" | |

abstract = True | |

numThreads = Param.Unsigned(Parent.numThreads, "Number of threads") | |

class SimpleIndirectPredictor(IndirectPredictor): | |

type = 'SimpleIndirectPredictor' | |

cxx_class = 'SimpleIndirectPredictor' | |

cxx_header = "cpu/pred/simple_indirect.hh" | |

indirectHashGHR = Param.Bool(True, "Hash branch predictor GHR") | |

indirectHashTargets = Param.Bool(True, "Hash path history targets") | |

indirectSets = Param.Unsigned(256, "Cache sets for indirect predictor") | |

indirectWays = Param.Unsigned(2, "Ways for indirect predictor") | |

indirectTagSize = Param.Unsigned(16, "Indirect target cache tag bits") | |

indirectPathLength = Param.Unsigned(3, | |

"Previous indirect targets to use for path history") | |

indirectGHRBits = Param.Unsigned(13, "Indirect GHR number of bits") | |

instShiftAmt = Param.Unsigned(2, "Number of bits to shift instructions by") | |

class BranchPredictor(SimObject): | |

type = 'BranchPredictor' | |

cxx_class = 'BPredUnit' | |

cxx_header = "cpu/pred/bpred_unit.hh" | |

abstract = True | |

numThreads = Param.Unsigned(Parent.numThreads, "Number of threads") | |

BTBEntries = Param.Unsigned(4096, "Number of BTB entries") | |

BTBTagSize = Param.Unsigned(16, "Size of the BTB tags, in bits") | |

RASSize = Param.Unsigned(16, "RAS size") | |

instShiftAmt = Param.Unsigned(2, "Number of bits to shift instructions by") | |

indirectBranchPred = Param.IndirectPredictor(SimpleIndirectPredictor(), | |

"Indirect branch predictor, set to NULL to disable indirect predictions") | |

class LocalBP(BranchPredictor): | |

type = 'LocalBP' | |

cxx_class = 'LocalBP' | |

cxx_header = "cpu/pred/2bit_local.hh" | |

localPredictorSize = Param.Unsigned(2048, "Size of local predictor") | |

localCtrBits = Param.Unsigned(2, "Bits per counter") | |

class TournamentBP(BranchPredictor): | |

type = 'TournamentBP' | |

cxx_class = 'TournamentBP' | |

cxx_header = "cpu/pred/tournament.hh" | |

localPredictorSize = Param.Unsigned(2048, "Size of local predictor") | |

localCtrBits = Param.Unsigned(2, "Bits per counter") | |

localHistoryTableSize = Param.Unsigned(2048, "size of local history table") | |

globalPredictorSize = Param.Unsigned(8192, "Size of global predictor") | |

globalCtrBits = Param.Unsigned(2, "Bits per counter") | |

choicePredictorSize = Param.Unsigned(8192, "Size of choice predictor") | |

choiceCtrBits = Param.Unsigned(2, "Bits of choice counters") | |

class BiModeBP(BranchPredictor): | |

type = 'BiModeBP' | |

cxx_class = 'BiModeBP' | |

cxx_header = "cpu/pred/bi_mode.hh" | |

globalPredictorSize = Param.Unsigned(8192, "Size of global predictor") | |

globalCtrBits = Param.Unsigned(2, "Bits per counter") | |

choicePredictorSize = Param.Unsigned(8192, "Size of choice predictor") | |

choiceCtrBits = Param.Unsigned(2, "Bits of choice counters") | |

class TAGEBase(SimObject): | |

type = 'TAGEBase' | |

cxx_class = 'TAGEBase' | |

cxx_header = "cpu/pred/tage_base.hh" | |

numThreads = Param.Unsigned(Parent.numThreads, "Number of threads") | |

instShiftAmt = Param.Unsigned(Parent.instShiftAmt, | |

"Number of bits to shift instructions by") | |

nHistoryTables = Param.Unsigned(7, "Number of history tables") | |

minHist = Param.Unsigned(5, "Minimum history size of TAGE") | |

maxHist = Param.Unsigned(130, "Maximum history size of TAGE") | |

tagTableTagWidths = VectorParam.Unsigned( | |

[0, 9, 9, 10, 10, 11, 11, 12], "Tag size in TAGE tag tables") | |

logTagTableSizes = VectorParam.Int( | |

[13, 9, 9, 9, 9, 9, 9, 9], "Log2 of TAGE table sizes") | |

logRatioBiModalHystEntries = Param.Unsigned(2, | |

"Log num of prediction entries for a shared hysteresis bit " \ | |

"for the Bimodal") | |

tagTableCounterBits = Param.Unsigned(3, "Number of tag table counter bits") | |

tagTableUBits = Param.Unsigned(2, "Number of tag table u bits") | |

histBufferSize = Param.Unsigned(2097152, | |

"A large number to track all branch histories(2MEntries default)") | |

pathHistBits = Param.Unsigned(16, "Path history size") | |

logUResetPeriod = Param.Unsigned(18, | |

"Log period in number of branches to reset TAGE useful counters") | |

numUseAltOnNa = Param.Unsigned(1, "Number of USE_ALT_ON_NA counters") | |

useAltOnNaBits = Param.Unsigned(4, "Size of the USE_ALT_ON_NA counter(s)") | |

maxNumAlloc = Param.Unsigned(1, | |

"Max number of TAGE entries allocted on mispredict") | |

# List of enabled TAGE tables. If empty, all are enabled | |

noSkip = VectorParam.Bool([], "Vector of enabled TAGE tables") | |

speculativeHistUpdate = Param.Bool(True, | |

"Use speculative update for histories") | |

# TAGE branch predictor as described in https://www.jilp.org/vol8/v8paper1.pdf | |

# The default sizes below are for the 8C-TAGE configuration (63.5 Kbits) | |

class TAGE(BranchPredictor): | |

type = 'TAGE' | |

cxx_class = 'TAGE' | |

cxx_header = "cpu/pred/tage.hh" | |

tage = Param.TAGEBase(TAGEBase(), "Tage object") | |

class LTAGE_TAGE(TAGEBase): | |

nHistoryTables = 12 | |

minHist = 4 | |

maxHist = 640 | |

tagTableTagWidths = [0, 7, 7, 8, 8, 9, 10, 11, 12, 12, 13, 14, 15] | |

logTagTableSizes = [14, 10, 10, 11, 11, 11, 11, 10, 10, 10, 10, 9, 9] | |

logUResetPeriod = 19 | |

class LoopPredictor(SimObject): | |

type = 'LoopPredictor' | |

cxx_class = 'LoopPredictor' | |

cxx_header = 'cpu/pred/loop_predictor.hh' | |

logSizeLoopPred = Param.Unsigned(8, "Log size of the loop predictor") | |

withLoopBits = Param.Unsigned(7, "Size of the WITHLOOP counter") | |

loopTableAgeBits = Param.Unsigned(8, "Number of age bits per loop entry") | |

loopTableConfidenceBits = Param.Unsigned(2, | |

"Number of confidence bits per loop entry") | |

loopTableTagBits = Param.Unsigned(14, "Number of tag bits per loop entry") | |

loopTableIterBits = Param.Unsigned(14, "Nuber of iteration bits per loop") | |

logLoopTableAssoc = Param.Unsigned(2, "Log loop predictor associativity") | |

# Parameters for enabling modifications to the loop predictor | |

# They have been copied from TAGE-GSC-IMLI | |

# (http://www.irisa.fr/alf/downloads/seznec/TAGE-GSC-IMLI.tar) | |

# | |

# All of them should be disabled to match the original LTAGE implementation | |

# (http://hpca23.cse.tamu.edu/taco/camino/cbp2/cbp-src/realistic-seznec.h) | |

# Add speculation | |

useSpeculation = Param.Bool(False, "Use speculation") | |

# Add hashing for calculating the loop table index | |

useHashing = Param.Bool(False, "Use hashing") | |

# Add a direction bit to the loop table entries | |

useDirectionBit = Param.Bool(False, "Use direction info") | |

# If true, use random to decide whether to allocate or not, and only try | |

# with one entry | |

restrictAllocation = Param.Bool(False, | |

"Restrict the allocation conditions") | |

initialLoopIter = Param.Unsigned(1, "Initial iteration number") | |

initialLoopAge = Param.Unsigned(255, "Initial age value") | |

optionalAgeReset = Param.Bool(True, | |

"Reset age bits optionally in some cases") | |

class TAGE_SC_L_TAGE(TAGEBase): | |

type = 'TAGE_SC_L_TAGE' | |

cxx_class = 'TAGE_SC_L_TAGE' | |

cxx_header = "cpu/pred/tage_sc_l.hh" | |

abstract = True | |

tagTableTagWidths = [0] | |

numUseAltOnNa = 16 | |

pathHistBits = 27 | |

maxNumAlloc = 2 | |

logUResetPeriod = 10 | |

useAltOnNaBits = 5 | |

# TODO No speculation implemented as of now | |

speculativeHistUpdate = False | |

# This size does not set the final sizes of the tables (it is just used | |

# for some calculations) | |

# Instead, the number of TAGE entries comes from shortTagsTageEntries and | |

# longTagsTageEntries | |

logTagTableSize = Param.Unsigned("Log size of each tag table") | |

shortTagsTageFactor = Param.Unsigned( | |

"Factor for calculating the total number of short tags TAGE entries") | |

longTagsTageFactor = Param.Unsigned( | |

"Factor for calculating the total number of long tags TAGE entries") | |

shortTagsSize = Param.Unsigned(8, "Size of the short tags") | |

longTagsSize = Param.Unsigned("Size of the long tags") | |

firstLongTagTable = Param.Unsigned("First table with long tags") | |

truncatePathHist = Param.Bool(True, | |

"Truncate the path history to its configured size") | |

class TAGE_SC_L_TAGE_64KB(TAGE_SC_L_TAGE): | |

type = 'TAGE_SC_L_TAGE_64KB' | |

cxx_class = 'TAGE_SC_L_TAGE_64KB' | |

cxx_header = "cpu/pred/tage_sc_l_64KB.hh" | |

nHistoryTables = 36 | |

minHist = 6 | |

maxHist = 3000 | |

tagTableUBits = 1 | |

logTagTableSizes = [13] | |

# This is used to handle the 2-way associativity | |

# (all odd entries are set to one, and if the corresponding even entry | |

# is set to one, then there is a 2-way associativity for this pair) | |

# Entry 0 is for the bimodal and it is ignored | |

# Note: For this implementation, some odd entries are also set to 0 to save | |

# some bits | |

noSkip = [0,0,1,0,0,0,1,0,0,1,1,1,1,1,1,1,1,1,1, | |

1,1,1,1,0,1,0,1,0,1,0,0,0,1,0,0,0,1] | |

logTagTableSize = 10 | |

shortTagsTageFactor = 10 | |

longTagsTageFactor = 20 | |

longTagsSize = 12 | |

firstLongTagTable = 13 | |

class TAGE_SC_L_TAGE_8KB(TAGE_SC_L_TAGE): | |

type = 'TAGE_SC_L_TAGE_8KB' | |

cxx_class = 'TAGE_SC_L_TAGE_8KB' | |

cxx_header = "cpu/pred/tage_sc_l_8KB.hh" | |

nHistoryTables = 30 | |

minHist = 4 | |

maxHist = 1000 | |

logTagTableSize = 7 | |

shortTagsTageFactor = 9 | |

longTagsTageFactor = 17 | |

longTagsSize = 12 | |

logTagTableSizes = [12] | |

firstLongTagTable = 11 | |

truncatePathHist = False | |

noSkip = [0,0,1,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0,1,0,1,0,1,0,1] | |

tagTableUBits = 2 | |

# LTAGE branch predictor as described in | |

# https://www.irisa.fr/caps/people/seznec/L-TAGE.pdf | |

# It is basically a TAGE predictor plus a loop predictor | |

# The differnt TAGE sizes are updated according to the paper values (256 Kbits) | |

class LTAGE(TAGE): | |

type = 'LTAGE' | |

cxx_class = 'LTAGE' | |

cxx_header = "cpu/pred/ltage.hh" | |

tage = LTAGE_TAGE() | |

loop_predictor = Param.LoopPredictor(LoopPredictor(), "Loop predictor") | |

class TAGE_SC_L_LoopPredictor(LoopPredictor): | |

type = 'TAGE_SC_L_LoopPredictor' | |

cxx_class = 'TAGE_SC_L_LoopPredictor' | |

cxx_header = "cpu/pred/tage_sc_l.hh" | |

loopTableAgeBits = 4 | |

loopTableConfidenceBits = 4 | |

loopTableTagBits = 10 | |

loopTableIterBits = 10 | |

useSpeculation = False | |

useHashing = True | |

useDirectionBit = True | |

restrictAllocation = True | |

initialLoopIter = 0 | |

initialLoopAge = 7 | |

optionalAgeReset = False | |

class StatisticalCorrector(SimObject): | |

type = 'StatisticalCorrector' | |

cxx_class = 'StatisticalCorrector' | |

cxx_header = "cpu/pred/statistical_corrector.hh" | |

abstract = True | |

# Statistical corrector parameters | |

numEntriesFirstLocalHistories = Param.Unsigned( | |

"Number of entries for first local histories") | |

bwnb = Param.Unsigned("Num global backward branch GEHL lengths") | |

bwm = VectorParam.Int("Global backward branch GEHL lengths") | |

logBwnb = Param.Unsigned("Log num of global backward branch GEHL entries") | |

lnb = Param.Unsigned("Num first local history GEHL lenghts") | |

lm = VectorParam.Int("First local history GEHL lengths") | |

logLnb = Param.Unsigned("Log number of first local history GEHL entries") | |

inb = Param.Unsigned(1, "Num IMLI GEHL lenghts") | |

im = VectorParam.Int([8], "IMLI history GEHL lengths") | |

logInb = Param.Unsigned("Log number of IMLI GEHL entries") | |

logBias = Param.Unsigned("Log size of Bias tables") | |

logSizeUp = Param.Unsigned(6, | |

"Log size of update threshold counters tables") | |

chooserConfWidth = Param.Unsigned(7, | |

"Number of bits for the chooser counters") | |

updateThresholdWidth = Param.Unsigned(12, | |

"Number of bits for the update threshold counter") | |

pUpdateThresholdWidth = Param.Unsigned(8, | |

"Number of bits for the pUpdate threshold counters") | |

extraWeightsWidth = Param.Unsigned(6, | |

"Number of bits for the extra weights") | |

scCountersWidth = Param.Unsigned(6, "Statistical corrector counters width") | |

# TAGE-SC-L branch predictor as desribed in | |

# https://www.jilp.org/cbp2016/paper/AndreSeznecLimited.pdf | |

# It is a modified LTAGE predictor plus a statistical corrector predictor | |

# The TAGE modifications include bank interleaving and partial associativity | |

# Two different sizes are proposed in the paper: | |

# 8KB => See TAGE_SC_L_8KB below | |

# 64KB => See TAGE_SC_L_64KB below | |

# The TAGE_SC_L_8KB and TAGE_SC_L_64KB classes differ not only on the values | |

# of some parameters, but also in some implementation details | |

# Given this, the TAGE_SC_L class is left abstract | |

# Note that as it is now, this branch predictor does not handle any type | |

# of speculation: All the structures/histories are updated at commit time | |

class TAGE_SC_L(LTAGE): | |

type = 'TAGE_SC_L' | |

cxx_class = 'TAGE_SC_L' | |

cxx_header = "cpu/pred/tage_sc_l.hh" | |

abstract = True | |

statistical_corrector = Param.StatisticalCorrector( | |

"Statistical Corrector") | |

class TAGE_SC_L_64KB_LoopPredictor(TAGE_SC_L_LoopPredictor): | |

logSizeLoopPred = 5 | |

class TAGE_SC_L_8KB_LoopPredictor(TAGE_SC_L_LoopPredictor): | |

logSizeLoopPred = 3 | |

class TAGE_SC_L_64KB_StatisticalCorrector(StatisticalCorrector): | |

type = 'TAGE_SC_L_64KB_StatisticalCorrector' | |

cxx_class = 'TAGE_SC_L_64KB_StatisticalCorrector' | |

cxx_header = "cpu/pred/tage_sc_l_64KB.hh" | |

pnb = Param.Unsigned(3, "Num variation global branch GEHL lengths") | |

pm = VectorParam.Int([25, 16, 9], "Variation global branch GEHL lengths") | |

logPnb = Param.Unsigned(9, | |

"Log number of variation global branch GEHL entries") | |

snb = Param.Unsigned(3, "Num second local history GEHL lenghts") | |

sm = VectorParam.Int([16, 11, 6], "Second local history GEHL lengths") | |

logSnb = Param.Unsigned(9, | |

"Log number of second local history GEHL entries") | |

tnb = Param.Unsigned(2, "Num third local history GEHL lenghts") | |

tm = VectorParam.Int([9, 4], "Third local history GEHL lengths") | |

logTnb = Param.Unsigned(10, | |

"Log number of third local history GEHL entries") | |

imnb = Param.Unsigned(2, "Num second IMLI GEHL lenghts") | |

imm = VectorParam.Int([10, 4], "Second IMLI history GEHL lengths") | |

logImnb = Param.Unsigned(9, "Log number of second IMLI GEHL entries") | |

numEntriesSecondLocalHistories = Param.Unsigned(16, | |

"Number of entries for second local histories") | |

numEntriesThirdLocalHistories = Param.Unsigned(16, | |

"Number of entries for second local histories") | |

numEntriesFirstLocalHistories = 256 | |

logBias = 8 | |

bwnb = 3 | |

bwm = [40, 24, 10] | |

logBwnb = 10 | |

lnb = 3 | |

lm = [11, 6, 3] | |

logLnb = 10 | |

logInb = 8 | |

class TAGE_SC_L_8KB_StatisticalCorrector(StatisticalCorrector): | |

type = 'TAGE_SC_L_8KB_StatisticalCorrector' | |

cxx_class = 'TAGE_SC_L_8KB_StatisticalCorrector' | |

cxx_header = "cpu/pred/tage_sc_l_8KB.hh" | |

gnb = Param.Unsigned(2, "Num global branch GEHL lengths") | |

gm = VectorParam.Int([6, 3], "Global branch GEHL lengths") | |

logGnb = Param.Unsigned(7, "Log number of global branch GEHL entries") | |

numEntriesFirstLocalHistories = 64 | |

logBias = 7 | |

bwnb = 2 | |

logBwnb = 7 | |

bwm = [16, 8] | |

lnb = 2 | |

logLnb = 7 | |

lm = [6, 3] | |

logInb = 7 | |

# 64KB TAGE-SC-L branch predictor as described in | |

# http://www.jilp.org/cbp2016/paper/AndreSeznecLimited.pdf | |

class TAGE_SC_L_64KB(TAGE_SC_L): | |

type = 'TAGE_SC_L_64KB' | |

cxx_class = 'TAGE_SC_L_64KB' | |

cxx_header = "cpu/pred/tage_sc_l_64KB.hh" | |

tage = TAGE_SC_L_TAGE_64KB() | |

loop_predictor = TAGE_SC_L_64KB_LoopPredictor() | |

statistical_corrector = TAGE_SC_L_64KB_StatisticalCorrector() | |

# 8KB TAGE-SC-L branch predictor as described in | |

# http://www.jilp.org/cbp2016/paper/AndreSeznecLimited.pdf | |

class TAGE_SC_L_8KB(TAGE_SC_L): | |

type = 'TAGE_SC_L_8KB' | |

cxx_class = 'TAGE_SC_L_8KB' | |

cxx_header = "cpu/pred/tage_sc_l_8KB.hh" | |

tage = TAGE_SC_L_TAGE_8KB() | |

loop_predictor = TAGE_SC_L_8KB_LoopPredictor() | |

statistical_corrector = TAGE_SC_L_8KB_StatisticalCorrector() | |

class MultiperspectivePerceptron(BranchPredictor): | |

type = 'MultiperspectivePerceptron' | |

cxx_class = 'MultiperspectivePerceptron' | |

cxx_header = 'cpu/pred/multiperspective_perceptron.hh' | |

abstract = True | |

num_filter_entries = Param.Int("Number of filter entries") | |

num_local_histories = Param.Int("Number of local history entries") | |

local_history_length = Param.Int(11, | |

"Length in bits of each history entry") | |

block_size = Param.Int(21, | |

"number of ghist bits in a 'block'; this is the width of an initial " | |

"hash of ghist") | |

pcshift = Param.Int(-10, "Shift for hashing PC") | |

threshold = Param.Int(1, "Threshold for deciding low/high confidence") | |

bias0 = Param.Int(-5, | |

"Bias perceptron output this much on all-bits-zero local history") | |

bias1 = Param.Int(5, | |

"Bias perceptron output this much on all-bits-one local history") | |

biasmostly0 = Param.Int(-1, | |

"Bias perceptron output this much on almost-all-bits-zero local " | |

"history") | |

biasmostly1 = Param.Int(1, | |

"Bias perceptron output this much on almost-all-bits-one local " | |

"history") | |

nbest = Param.Int(20, | |

"Use this many of the top performing tables on a low-confidence " | |

"branch") | |

tunebits = Param.Int(24, "Number of bits in misprediction counters") | |

hshift = Param.Int(-6, | |

"How much to shift initial feauture hash before XORing with PC bits") | |

imli_mask1 = Param.UInt64( | |

"Which tables should have their indices hashed with the first IMLI " | |

"counter") | |

imli_mask4 = Param.UInt64( | |

"Which tables should have their indices hashed with the fourth IMLI " | |

"counter") | |

recencypos_mask = Param.UInt64( | |

"Which tables should have their indices hashed with the recency " | |

"position") | |

fudge = Param.Float(0.245, "Fudge factor to multiply by perceptron output") | |

n_sign_bits = Param.Int(2, "Number of sign bits per magnitude") | |

pcbit = Param.Int(2, "Bit from the PC to use for hashing global history") | |

decay = Param.Int(0, "Whether and how often to decay a random weight") | |

record_mask = Param.Int(191, | |

"Which histories are updated with filtered branch outcomes") | |

hash_taken = Param.Bool(False, | |

"Hash the taken/not taken value with a PC bit") | |

tuneonly = Param.Bool(True, | |

"If true, only count mispredictions of low-confidence branches") | |

extra_rounds = Param.Int(1, | |

"Number of extra rounds of training a single weight on a " | |

"low-confidence prediction") | |

speed = Param.Int(9, "Adaptive theta learning speed") | |

initial_theta = Param.Int(10, "Initial theta") | |

budgetbits = Param.Int("Hardware budget in bits") | |

speculative_update = Param.Bool(False, | |

"Use speculative update for histories") | |

class MultiperspectivePerceptron8KB(MultiperspectivePerceptron): | |

type = 'MultiperspectivePerceptron8KB' | |

cxx_class = 'MultiperspectivePerceptron8KB' | |

cxx_header = 'cpu/pred/multiperspective_perceptron_8KB.hh' | |

budgetbits = 8192 * 8 + 2048 | |

num_local_histories = 48 | |

num_filter_entries = 0 | |

imli_mask1 = 0x6 | |

imli_mask4 = 0x4400 | |

recencypos_mask = 0x100000090 | |

class MultiperspectivePerceptron64KB(MultiperspectivePerceptron): | |

type = 'MultiperspectivePerceptron64KB' | |

cxx_class = 'MultiperspectivePerceptron64KB' | |

cxx_header = 'cpu/pred/multiperspective_perceptron_64KB.hh' | |

budgetbits = 65536 * 8 + 2048 | |

num_local_histories = 510 | |

num_filter_entries = 18025 | |

imli_mask1 = 0xc1000 | |

imli_mask4 = 0x80008000 | |

recencypos_mask = 0x100000090 |