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# 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")
initialTCounterValue = Param.Int(1 << 17, "Initial value of tCounter")
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
initialTCounterValue = 1 << 9
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")
bwWeightInitValue = Param.Int(
"Initial value of the weights of the 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")
lWeightInitValue = Param.Int(
"Initial value of the weights of the 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")
iWeightInitValue = Param.Int(
"Initial value of the weights of the IMLI history 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")
initialUpdateThresholdValue = Param.Int(0,
"Initial pUpdate threshold counter value")
# 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
bwWeightInitValue = 7
lnb = 3
lm = [11, 6, 3]
logLnb = 10
lWeightInitValue = 7
logInb = 8
iWeightInitValue = 7
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]
bwWeightInitValue = 7
lnb = 2
logLnb = 7
lm = [6, 3]
lWeightInitValue = 7
logInb = 7
iWeightInitValue = 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")
initial_ghist_length = Param.Int(1, "Initial GHist length value")
ignore_path_size = Param.Bool(False, "Ignore the path storage")
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
class MPP_TAGE(TAGEBase):
type = 'MPP_TAGE'
cxx_class = 'MPP_TAGE'
cxx_header = 'cpu/pred/multiperspective_perceptron_tage.hh'
nHistoryTables = 15
pathHistBits = 27
instShiftAmt = 0
histBufferSize = 16384
maxHist = 4096;
tagTableTagWidths = [0, 7, 9, 9, 9, 10, 11, 11, 12, 12,
12, 13, 14, 15, 15, 15]
logTagTableSizes = [14, 10, 11, 11, 11, 11, 11, 12, 12,
10, 11, 11, 9, 7, 7, 8]
tunedHistoryLengths = VectorParam.Unsigned([0, 5, 12, 15, 21, 31, 43, 64,
93, 137, 200, 292, 424, 612, 877, 1241], "Tuned history lengths")
logUResetPeriod = 10
initialTCounterValue = 0
numUseAltOnNa = 512
speculativeHistUpdate = False
class MPP_LoopPredictor(LoopPredictor):
type = 'MPP_LoopPredictor'
cxx_class = 'MPP_LoopPredictor'
cxx_header = 'cpu/pred/multiperspective_perceptron_tage.hh'
useDirectionBit = True
useHashing = True
useSpeculation = False
loopTableConfidenceBits = 4
loopTableAgeBits = 4
initialLoopAge = 7
initialLoopIter = 0
loopTableIterBits = 12
optionalAgeReset = False
restrictAllocation = True
logSizeLoopPred = 6
loopTableTagBits = 10
class MPP_StatisticalCorrector(StatisticalCorrector):
type = 'MPP_StatisticalCorrector'
cxx_class = 'MPP_StatisticalCorrector'
cxx_header = 'cpu/pred/multiperspective_perceptron_tage.hh'
abstract = True
# Unused in this Statistical Corrector
bwnb = 0
bwm = [ ]
logBwnb = 0
bwWeightInitValue = -1
# Unused in this Statistical Corrector
logInb = 0
iWeightInitValue = -1
extraWeightsWidth = 0
pUpdateThresholdWidth = 10
initialUpdateThresholdValue = 35
logSizeUp = 5
lnb = 3
lm = [11, 6, 3]
logLnb = 10
lWeightInitValue = -1
gnb = Param.Unsigned(4, "Num global branch GEHL lengths")
gm = VectorParam.Int([27, 22, 17, 14], "Global branch GEHL lengths")
logGnb = Param.Unsigned(10, "Log number of global branch GEHL entries")
pnb = Param.Unsigned(4, "Num variation global branch GEHL lengths")
pm = VectorParam.Int([16, 11, 6, 3],
"Variation global branch GEHL lengths")
logPnb = Param.Unsigned(9,
"Log number of variation global branch GEHL entries")
class MultiperspectivePerceptronTAGE(MultiperspectivePerceptron):
type = 'MultiperspectivePerceptronTAGE'
cxx_class = 'MultiperspectivePerceptronTAGE'
cxx_header = 'cpu/pred/multiperspective_perceptron_tage.hh'
abstract = True
instShiftAmt = 4
imli_mask1 = 0x70
imli_mask4 = 0
num_filter_entries = 0
num_local_histories = 0
recencypos_mask = 0 # Unused
threshold = -1
initial_ghist_length = 0
ignore_path_size = True
n_sign_bits = 1;
tage = Param.TAGEBase("Tage object")
loop_predictor = Param.LoopPredictor("Loop predictor")
statistical_corrector = Param.StatisticalCorrector("Statistical Corrector")
class MPP_StatisticalCorrector_64KB(MPP_StatisticalCorrector):
type = 'MPP_StatisticalCorrector_64KB'
cxx_class = 'MPP_StatisticalCorrector_64KB'
cxx_header = 'cpu/pred/multiperspective_perceptron_tage_64KB.hh'
logBias = 8
snb = Param.Unsigned(4, "Num second local history GEHL lenghts")
sm = VectorParam.Int([16, 11, 6, 3], "Second local history GEHL lengths")
logSnb = Param.Unsigned(9,
"Log number of second local history GEHL entries")
tnb = Param.Unsigned(3, "Num third local history GEHL lenghts")
tm = VectorParam.Int([22, 17, 14], "Third local history GEHL lengths")
logTnb = Param.Unsigned(9,
"Log number of third local history 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
class MultiperspectivePerceptronTAGE64KB(MultiperspectivePerceptronTAGE):
type = 'MultiperspectivePerceptronTAGE64KB'
cxx_class = 'MultiperspectivePerceptronTAGE64KB'
cxx_header = 'cpu/pred/multiperspective_perceptron_tage_64KB.hh'
budgetbits = 65536 * 8 + 2048
tage = MPP_TAGE()
loop_predictor = MPP_LoopPredictor()
statistical_corrector = MPP_StatisticalCorrector_64KB()
class MPP_TAGE_8KB(MPP_TAGE):
type = 'MPP_TAGE_8KB'
cxx_class = 'MPP_TAGE_8KB'
cxx_header = 'cpu/pred/multiperspective_perceptron_tage_8KB.hh'
nHistoryTables = 10
tagTableTagWidths = [0, 7, 7, 7, 8, 9, 10, 10, 11, 13, 13]
logTagTableSizes = [12, 8, 8, 9, 9, 8, 8, 8, 7, 6, 7]
tunedHistoryLengths = [0, 4, 8, 13, 23, 36, 56, 93, 145, 226, 359]
class MPP_LoopPredictor_8KB(MPP_LoopPredictor):
type = 'MPP_LoopPredictor_8KB'
cxx_class = 'MPP_LoopPredictor_8KB'
cxx_header = 'cpu/pred/multiperspective_perceptron_tage_8KB.hh'
loopTableIterBits = 10
logSizeLoopPred = 4
class MPP_StatisticalCorrector_8KB(MPP_StatisticalCorrector):
type = 'MPP_StatisticalCorrector_8KB'
cxx_class = 'MPP_StatisticalCorrector_8KB'
cxx_header = 'cpu/pred/multiperspective_perceptron_tage_8KB.hh'
logBias = 7
lnb = 2
lm = [8, 3]
logLnb = 9
logGnb = 9
logPnb = 7
numEntriesFirstLocalHistories = 64
class MultiperspectivePerceptronTAGE8KB(MultiperspectivePerceptronTAGE):
type = 'MultiperspectivePerceptronTAGE8KB'
cxx_class = 'MultiperspectivePerceptronTAGE8KB'
cxx_header = 'cpu/pred/multiperspective_perceptron_tage_8KB.hh'
budgetbits = 8192 * 8 + 2048
tage = MPP_TAGE_8KB()
loop_predictor = MPP_LoopPredictor_8KB()
statistical_corrector = MPP_StatisticalCorrector_8KB()