from functools import reduce from operator import add from math import cos, pi from scipy import signal import matplotlib.pyplot as plt from migen import * from migen.fhdl import verilog # A synthesizable FIR filter. class FIR(Module): def __init__(self, coef, wsize=16): self.coef = coef self.wsize = wsize self.i = Signal((self.wsize, True)) self.o = Signal((self.wsize, True)) ### muls = [] src = self.i for c in self.coef: sreg = Signal((self.wsize, True)) self.sync += sreg.eq(src) src = sreg c_fp = int(c*2**(self.wsize - 1)) muls.append(c_fp*sreg) sum_full = Signal((2*self.wsize-1, True)) self.sync += sum_full.eq(reduce(add, muls)) self.comb += self.o.eq(sum_full >> self.wsize-1) # A test bench for our FIR filter. # Generates a sine wave at the input and records the output. def fir_tb(dut, frequency, inputs, outputs): f = 2**(dut.wsize - 1) for cycle in range(200): v = 0.1*cos(2*pi*frequency*cycle) yield dut.i.eq(int(f*v)) inputs.append(v) outputs.append((yield dut.o)/f) yield if __name__ == "__main__": # Compute filter coefficients with SciPy. coef = signal.remez(30, [0, 0.1, 0.2, 0.4, 0.45, 0.5], [0, 1, 0]) # Simulate for different frequencies and concatenate # the results. in_signals = [] out_signals = [] for frequency in [0.05, 0.1, 0.25]: dut = FIR(coef) tb = fir_tb(dut, frequency, in_signals, out_signals) run_simulation(dut, tb) # Plot data from the input and output waveforms. plt.plot(in_signals) plt.plot(out_signals) plt.show() # Print the Verilog source for the filter. fir = FIR(coef) print(verilog.convert(fir, ios={fir.i, fir.o}))