39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
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from nmigen import *
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__all__ = ["BranchPredictor"]
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class BranchPredictor(Elaboratable):
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def __init__(self):
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self.d_branch = Signal()
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self.d_jump = Signal()
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self.d_offset = Signal((32, True))
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self.d_pc = Signal(32)
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self.d_rs1_re = Signal()
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self.d_branch_taken = Signal()
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self.d_branch_target = Signal(32)
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def elaborate(self, platform):
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m = Module()
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d_fetch_misaligned = Signal()
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m.d.comb += [
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d_fetch_misaligned.eq(self.d_pc[:2].bool() | self.d_offset[:2].bool()),
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self.d_branch_target.eq(self.d_pc + self.d_offset),
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]
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with m.If(d_fetch_misaligned):
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m.d.comb += self.d_branch_taken.eq(0)
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with m.Elif(self.d_branch):
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# Backward conditional branches are predicted as taken.
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# Forward conditional branches are predicted as not taken.
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m.d.comb += self.d_branch_taken.eq(self.d_offset[-1])
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with m.Else():
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# Direct jumps are predicted as taken.
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# Other branch types (ie. indirect jumps, exceptions) are not predicted.
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m.d.comb += self.d_branch_taken.eq(self.d_jump & ~self.d_rs1_re)
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return m
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