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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 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")
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")
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")
# 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")
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