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fir: streamline, optimize DSP extraction, left-align inputs

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This commit is contained in:
Robert Jördens 2016-12-20 21:39:51 +01:00
parent cfb66117af
commit f5f662200b
3 changed files with 61 additions and 108 deletions
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121
artiq/gateware/dsp/fir.py
View File

@ -1,6 +1,10 @@
from math import floor
from operator import add
from functools import reduce
from collections import namedtuple
import numpy as np
from migen import *
@ -40,56 +44,11 @@ def halfgen4(width, n, df=1e-3):
return a
class FIR(Module):
"""Full-rate finite impulse response filter.
_Widths = namedtuple("_Widths", "A B P")
Tries to use transposed form (adder chain instead of adder tree)
as much as possible.
:param coefficients: integer taps, increasing delay.
:param width: bit width of input and output.
:param shift: scale factor (as power of two).
"""
def __init__(self, coefficients, width=16, shift=None):
self.width = width
self.i = Signal((width, True))
self.o = Signal((width, True))
n = len(coefficients)
self.latency = n//2 + 3
###
if shift is None:
shift = bits_for(sum(abs(c) for c in coefficients)) - 1
# Delay line: increasing delay
x = [Signal((width, True)) for _ in range(n)]
self.sync += [xi.eq(xj) for xi, xj in zip(x, [self.i] + x)]
o = Signal((width + shift + 1, True))
self.comb += self.o.eq(o >> shift)
delay = -1
# Make products
for i, c in enumerate(coefficients):
# simplify for halfband and symmetric filters
if not c or c in coefficients[:i]:
continue
js = [j for j, cj in enumerate(coefficients) if cj == c]
m = Signal.like(o)
o0, o = o, Signal.like(o)
if delay < js[0]:
self.sync += o0.eq(o + m)
delay += 1
else:
self.comb += o0.eq(o + m)
assert js[0] - delay >= 0
xs = [x[j - delay] for j in js]
s = Signal((bits_for(len(xs)) - 1 + len(xs[0]), True))
self.comb += s.eq(sum(xs))
self.sync += m.eq(c*s)
# symmetric rounding
if shift:
self.comb += o.eq((1 << shift - 1) - 1)
_widths = {
"DSP48E1": _Widths(25, 18, 48),
}
class ParallelFIR(Module):
@ -97,12 +56,14 @@ class ParallelFIR(Module):
Tries to use transposed form as much as possible.
:param coefficients: integer taps, increasing delay.
:param coefficients: tap coefficients (normalized to 1.),
increasing delay.
:param parallelism: number of samples per cycle.
:param width: bit width of input and output.
:param shift: scale factor (as power of two).
:param arch: architecture (default: "DSP48E1").
"""
def __init__(self, coefficients, parallelism, width=16, shift=None):
def __init__(self, coefficients, parallelism, width=16,
arch="DSP48E1"):
self.width = width
self.parallelism = p = parallelism
n = len(coefficients)
@ -111,45 +72,60 @@ class ParallelFIR(Module):
self.o = [Signal((width, True)) for i in range(p)]
self.latency = (n + 1)//2//p + 2
# ... plus one sample
w = _widths[arch]
c_max = max(abs(c) for c in coefficients)
c_shift = bits_for(floor((1 << w.B - 2) / c_max))
self.coefficients = cs = [int(round(c*(1 << c_shift)))
for c in coefficients]
###
if shift is None:
shift = bits_for(sum(abs(c) for c in coefficients)) - 1
# Delay line: increasing delay
x = [Signal((width, True)) for _ in range(n + p - 1)]
self.sync += [xi.eq(xj) for xi, xj in zip(x, self.i[::-1] + x)]
x = [Signal((w.A, True)) for _ in range(n + p - 1)]
x_shift = w.A - width - bits_for(
max(cs.count(c) for c in cs if c) - 1)
for xi, xj in zip(x, self.i[::-1]):
self.sync += xi.eq(xj << x_shift)
for xi, xj in zip(x[len(self.i):], x):
self.sync += xi.eq(xj)
for delay in range(p):
o = Signal((width + shift + 1, True))
self.comb += self.o[delay].eq(o >> shift)
o = Signal((w.P, True))
self.comb += self.o[delay].eq(o >> c_shift + x_shift)
# Make products
for i, c in enumerate(coefficients):
for i, c in enumerate(cs):
# simplify for halfband and symmetric filters
if not c or c in coefficients[:i]:
if not c or c in cs[:i]:
continue
js = [j + p - 1 for j, cj in enumerate(coefficients)
if cj == c]
js = [j + p - 1 for j, cj in enumerate(cs) if cj == c]
m = Signal.like(o)
o0, o = o, Signal.like(o)
q = Signal.like(x[0])
if delay + p <= js[0]:
self.sync += o0.eq(o + m)
delay += p
else:
self.comb += o0.eq(o + m)
assert js[0] - delay >= 0
xs = [x[j - delay] for j in js]
s = Signal((bits_for(len(xs)) - 1 + len(xs[0]), True))
self.comb += s.eq(sum(xs))
self.sync += m.eq(c*s)
self.comb += q.eq(reduce(add, [x[j - delay] for j in js]))
self.sync += m.eq(c*q)
# symmetric rounding
if shift:
self.comb += o.eq((1 << shift - 1) - 1)
if c_shift + x_shift > 1:
self.comb += o.eq((1 << c_shift + x_shift - 1) - 1)
class FIR(ParallelFIR):
def __init__(self, *args, **kwargs):
super().__init__(self, *args, parallelism=1, **kwargs)
self.i = self.i[0]
self.o = self.o[0]
def halfgen4_cascade(rate, width, order=None):
"""Generate coefficients for cascaded half-band filters.
Coefficients are normalized to a gain of two per stage to compensate for
the zero stuffing.
:param rate: upsampling rate. power of two
:param width: passband/stopband width in units of input sampling rate.
@ -160,7 +136,7 @@ def halfgen4_cascade(rate, width, order=None):
p = 1
while p < rate:
p *= 2
coeff.append(halfgen4(width*p/rate/2, order*p//rate))
coeff.append(2*halfgen4(width*p/rate/2, order*p//rate))
return coeff
@ -170,8 +146,8 @@ class ParallelHBFUpsampler(Module):
Coefficients should be normalized to overall gain of 2
(highest/center coefficient being 1)."""
def __init__(self, coefficients, width=16, **kwargs):
self.parallelism = 1
self.latency = 0
self.parallelism = 1 # accumulate
self.latency = 0 # accumulate
self.width = width
self.i = Signal((width, True))
@ -180,7 +156,6 @@ class ParallelHBFUpsampler(Module):
i = [self.i]
for coeff in coefficients:
self.parallelism *= 2
# assert coeff[len(coeff)//2 + 1] == 1
hbf = ParallelFIR(coeff, self.parallelism, width, **kwargs)
self.submodules += hbf
self.comb += [a.eq(b) for a, b in zip(hbf.i[::2], i)]

6
artiq/gateware/dsp/sawg.py
View File

@ -128,10 +128,8 @@ class Channel(Module, SatAddMixin):
self.submodules.a1 = a1 = SplineParallelDDS(widths, orders)
self.submodules.a2 = a2 = SplineParallelDDS(widths, orders)
coeff = [[int(round((1 << 18)*ci)) for ci in c]
for c in halfgen4_cascade(parallelism, width=.4, order=8)]
hbf = [ParallelHBFUpsampler(coeff, width=width, shift=17)
for i in range(2)]
coeff = halfgen4_cascade(parallelism, width=.4, order=8)
hbf = [ParallelHBFUpsampler(coeff, width=width) for i in range(2)]
self.submodules.b = b = SplineParallelDUC(
widths._replace(a=len(hbf[0].o[0]), f=widths.f - width), orders,
parallelism=parallelism)

42
artiq/test/gateware/fir.py
View File

@ -11,16 +11,16 @@ class Transfer(Module):
self.submodules.dut = dut
def drive(self, x):
for xi in x:
yield self.dut.i.eq(int(xi))
for xi in x.reshape(-1, self.dut.parallelism):
yield [ij.eq(int(xj)) for ij, xj in zip(self.dut.i, xi)]
yield
def record(self, y):
for i in range(self.dut.latency):
yield
for i in range(len(y)):
for yi in y.reshape(-1, self.dut.parallelism):
yield
y[i] = (yield self.dut.o)
yi[:] = (yield from [(yield o) for o in self.dut.o])
def run(self, samples, amplitude=1.):
w = 2**(self.dut.width - 1) - 1
@ -63,21 +63,7 @@ class Transfer(Module):
return fig
class ParallelTransfer(Transfer):
def drive(self, x):
for xi in x.reshape(-1, self.dut.parallelism):
yield [ij.eq(int(xj)) for ij, xj in zip(self.dut.i, xi)]
yield
def record(self, y):
for i in range(self.dut.latency):
yield
for yi in y.reshape(-1, self.dut.parallelism):
yield
yi[:] = (yield from [(yield o) for o in self.dut.o])
class UpTransfer(ParallelTransfer):
class UpTransfer(Transfer):
def drive(self, x):
x = x.reshape(-1, len(self.dut.o))
x[:, 1:] = 0
@ -94,21 +80,15 @@ class UpTransfer(ParallelTransfer):
def _main():
coeff = fir.halfgen4(.4/2, 8)
coeff_int = [int(round(c * (1 << 16 - 1))) for c in coeff]
if False:
coeff = [[int(round((1 << 19) * ci)) for ci in c]
for c in fir.halfgen4_cascade(8, width=.4, order=8)]
dut = fir.ParallelHBFUpsampler(coeff, width=16, shift=18)
if True:
coeff = fir.halfgen4_cascade(8, width=.4, order=8)
dut = fir.ParallelHBFUpsampler(coeff, width=16)
# print(verilog.convert(dut, ios=set([dut.i] + dut.o)))
tb = UpTransfer(dut)
elif True:
dut = fir.ParallelFIR(coeff_int, parallelism=4, width=16)
# print(verilog.convert(dut, ios=set(dut.i + dut.o)))
tb = ParallelTransfer(dut)
else:
dut = fir.FIR(coeff_int, width=16)
# print(verilog.convert(dut, ios={dut.i, dut.o}))
coeff = fir.halfgen4(.4/2, 8)
dut = fir.ParallelFIR(coeff, parallelism=4, width=16)
# print(verilog.convert(dut, ios=set(dut.i + dut.o)))
tb = Transfer(dut)
x, y = tb.run(samples=1 << 10, amplitude=.5)