477a478,559
>
> 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
>
> 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