src/cpu/pred/BranchPredictor.py - testing/jenkins-gem5-prod - Git at Google

 # 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 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")

     useIndirect = Param.Bool(True, "Use indirect branch predictor")
     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")


 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()
	# 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 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")

	useIndirect = Param.Bool(True, "Use indirect branch predictor")
	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")



	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()